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Wang, Lei, Liang Zeng, and Jian Li. "AEC-GAN: Adversarial Error Correction GANs for Auto-Regressive Long Time-Series Generation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 10140–48. http://dx.doi.org/10.1609/aaai.v37i8.26208.
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Large-scale high-quality data is critical for training modern deep neural networks. However, data acquisition can be costly or time-consuming for many time-series applications, thus researchers turn to generative models for generating synthetic time-series data. In particular, recent generative adversarial networks (GANs) have achieved remarkable success in time-series generation. Despite their success, existing GAN models typically generate the sequences in an auto-regressive manner, and we empirically observe that they suffer from severe distribution shifts and bias amplification, especially when generating long sequences. To resolve this problem, we propose Adversarial Error Correction GAN (AEC-GAN), which is capable of dynamically correcting the bias in the past generated data to alleviate the risk of distribution shifts and thus can generate high-quality long sequences. AEC-GAN contains two main innovations: (1) We develop an error correction module to mitigate the bias. In the training phase, we adversarially perturb the realistic time-series data and then optimize this module to reconstruct the original data. In the generation phase, this module can act as an efficient regulator to detect and mitigate the bias. (2) We propose an augmentation method to facilitate GAN's training by introducing adversarial examples. Thus, AEC-GAN can generate high-quality sequences of arbitrary lengths, and the synthetic data can be readily applied to downstream tasks to boost their performance. We conduct extensive experiments on six widely used datasets and three state-of-the-art time-series forecasting models to evaluate the quality of our synthetic time-series data in different lengths and downstream tasks. Both the qualitative and quantitative experimental results demonstrate the superior performance of AEC-GAN over other deep generative models for time-series generation.
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Shah, Chirag Vinalbhai. "Exploring Generative Adversarial Networks: Applications of GANs in AI and Big Data." Global Research and Development Journals 7, no. 5 (2022): 11–20. http://dx.doi.org/10.70179/grdjev09i100011.
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This chapter explores generative adversarial networks (GANs), which simultaneously train two models: a generator (G) to generate new instances that resemble a training data set; and a discriminator (D) that estimates the probability that an instance was produced by the generator rather than belonging to the training data. The capabilities of GANs are explored, thrusting GANs into the forefront of AI research and development. Preparation of the data input to the generative adversarial network occurs before the application of the discriminator to the multilayer perceptron neural network designed to solve an example classification problem. Then, utilizing GANs to facilitate big data implementation is suggested, simultaneously imputing the missing data and selecting a smaller more effective training set for an example classification problem. AI problems can be defined as realizations of various data models using a multilayer perceptron neural network, which are here illustrated and employed to solve the "and" (AND) function approximation, a famous AI hard problem. Also defined and described. Note that multi-GANs are employed to select an overall effective representative training data set for classification problems, facilitate big data, and predict missing data values during the preparation of the training set. This work differs from the recent efforts attempting to improve GANs. A hyperparameter tuning methodology. Furthermore, this paper couples the imputation of missing no-label binary data with data in the training set used as an input to a multilayer perceptron neural network, which has typically shown an improved estimate of the models needed for binary and optimization results when samples are selected to represent the "and" (AND) function classification problem training process for mid- and large-scale problems.
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Ambu, Karthik, Shetty Jyoti, G. Shobha, and Dev Roger. "Implementation of generative adversarial networks in HPCC systems using GNN bundle." International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (2021): 374–81. https://doi.org/10.11591/ijai.v10.i2.pp374-381.
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HPCC systems, an open source cluster computing platform for big data analytics consists of generalized neural network bundle with a wide variety of features which can be used for various neural network applications. To enhance the functionality of the bundle, this paper proposes the design and development of generative adversarial networks (GANs) on HPCC systems platform using ECL, a declarative language on which HPCC systems works. GANs have been developed on the HPCC platform by defining the generator and discriminator models separately, and training them by batches in the same epoch. In order to make sure that they train as adversaries, a certain weights transfer methodology was implemented. MNIST dataset which has been used to test the proposed approach has provided satisfactory results. The results obtained were unique images very similar to the MNIST dataset, as it were expected.
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Purwono, Purwono, Annastasya Nabila Elsa Wulandari, Alfian Ma'arif, and Wael A. Salah. "Understanding Generative Adversarial Networks (GANs): A Review." Control Systems and Optimization Letters 3, no. 1 (2025): 36–45. https://doi.org/10.59247/csol.v3i1.170.
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Generative Adversarial Networks (GANs) is an important breakthrough in artificial intelligence that uses two neural networks, a generator and a discriminator, that work in an adversarial framework. The generator generates synthetic data, while the discriminator evaluates the authenticity of the data. This dynamic interaction forms a minimax game that produces high-quality synthetic data. Since its introduction in 2014 by Ian Goodfellow, GAN has evolved through various innovative architectures, including Vanilla GAN, Conditional GAN (cGAN), Deep Convolutional GAN (DCGAN), CycleGAN, StyleGAN, Wasserstein GAN (WGAN), and BigGAN. Each of these architectures presents a novel approach to address technical challenges such as training stability, data diversification, and result quality. GANs have been widely applied in various sectors. In healthcare, GANs are used to generate synthetic medical images that support diagnostic development without violating patient privacy. In the media and entertainment industry, GANs facilitate the enhancement of image and video resolution, as well as the creation of realistic content. However, the development of GANs faces challenges such as mode collapse, training instability, and inadequate quality evaluation. In addition to technical challenges, GANs raise ethical issues, such as the misuse of the technology for deepfake creation. Legal regulations, detection tools, and public education are important mitigation measures. Future trends suggest that GANs will be increasingly used in text-to-image synthesis, realistic video generation, and integration with multimodal systems to support cross-disciplinary innovation.
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Aynaan, Quraishi, Jethwa Jaydeep, and Gupta Shiwani. "Synthetic audio and video generation for language translation using GANs." i-manager's Journal on Augmented & Virtual Reality 1, no. 1 (2023): 1. http://dx.doi.org/10.26634/javr.1.1.19412.
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Language barriers create a digital divide that prevents people from benefiting from the vast amount of content produced worldwide. In addition, content creators face challenges in producing content in multiple languages to reach a wider audience. To address this problem, this study proposed a solution through a survey that utilized Generative Adversarial Networks (GAN), Natural Language Processing (NPL), and Computer Vision. A Generative Adversarial Network (GAN) is a Machine Learning (ML) model in which two neural networks compete with each other by using deep learning methods to obtain more accurate predictions. The solution provided in this study can generate synthesized videos that are close to reality, ultimately bridging the language barrier and providing access to content.
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Alanazi, Meshari Huwaytim. "G-GANS for Adaptive Learning in Dynamic Network Slices." Engineering, Technology & Applied Science Research 14, no. 3 (2024): 14327–41. http://dx.doi.org/10.48084/etasr.7046.
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This paper introduces a novel approach to improve security in dynamic network slices for 5G networks using Graph-based Generative Adversarial Networks (G-GAN). Given the rapidly evolving and adaptable nature of 5G network slices, traditional security mechanisms often fall short in providing real-time, efficient, and scalable defense mechanisms. To address this gap, this study proposes the use of G-GAN, which combines the strengths of Generative Adversarial Networks (GANs) and Graph Neural Networks (GNNs) for adaptive learning and anomaly detection in dynamic network environments. The proposed approach utilizes GAN to generate realistic network traffic patterns, both normal and adversarial, whereas GNNs analyze these patterns within the context of the network's graph-based topology. This combination facilitates the early detection of anomalies and potential security threats, adapting to the ever-changing configurations of network slices. The current study presents a comprehensive methodology for implementing G-GAN, including system architecture, data processing, and model training. The experimental analysis demonstrates the efficacy of G-GAN in accurately identifying security threats and adapting to new scenarios, revealing that G-GAN outperformed established models with an accuracy of 97.12%, precision of 96.20%, recall of 97.24%, and F1-Score of 96.72%. This study not only contributes to the field of network security in the context of 5G, but also opens avenues for future exploration in the application of hybrid AI models for real-time security across various domains.
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Zhang, Youliang, Guowu Yuan, Hao Wu, and Hao Zhou. "MAE-GAN: a self-supervised learning-based classification model for cigarette appearance defects." Applied Computing and Intelligence 4, no. 2 (2024): 253–68. https://doi.org/10.3934/aci.2024015.
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<p>Appearance defects frequently occur during cigarette manufacturing due to production equipment or raw materials. Appearance defects significantly impact the quality of tobacco products. Since manual inspection cannot keep pace with the demands of high-speed production lines, rapid and accurate automated classification and detection are essential. Supervised learning is predominantly employed in research on automated classification of product quality appearance defects. However, supervised learning necessitates substantial labeled data for training, which is time-consuming to annotate and prone to errors. This paper proposes a self-supervised learning-based classification model for cigarette appearance defects. This is a generative adversarial network (GAN) model based on masked autoencoders (MAE), called MAE-GAN. First, this model combines MAE as a generator with a simple discriminator to form a generative adversarial network according to the principle of mask reconstruction in MAE. The generator reconstructs the images to learn their features. Second, the model also integrates MAE's loss function into the GAN's loss function. This lets the model focus on pixel-level losses during training. As a result, model performance is improved. Third, a Wasserstein GAN with gradient penalty (WGAN-GP) is added to stabilize the training process. In addition, this paper preprocesses cigarette images through segmentation and recombination. Neural networks typically accept images with the same width and height. Due to the narrow shape of cigarette images, if the image is directly transformed into a square and fed into a neural network, the details of the image will be severely lost. This paper segments the cigarette image into three parts and recombines them into images with similar length and width, greatly improving classification accuracy.</p>
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El-Shal, Ibrahim H., Omar M. Fahmy, and Mustafa A. Elattar. "License Plate Image Analysis Empowered by Generative Adversarial Neural Networks (GANs)." IEEE Access 10 (2022): 30846–57. http://dx.doi.org/10.1109/access.2022.3157714.
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Vyshnavi, Tiramdasu, and Jatinder Kaur. "Decipherment of script - sing synthetic data generator’s." Journal of Information and Optimization Sciences 46, no. 3 (2025): 783–91. https://doi.org/10.47974/jios-1797.
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This paper is created to give a brief idea about the generative adversarial networks and the handwritten digits recognition using the synthetic data generators with the MNIST data. In this paper Tensorflow is used along with GAN’s to recognise the handwritten digits. The 2014 introduction of Generative Adversarial Networks (GANs) by Lan J. Goodfellow revolutionized deep learning and artificial intelligence. A subclass of generative models known as GANs uses a dual neural network structure—a generator and a discriminator— to transform unsupervised learning. GANs create varied, real data samples, influencing the design, entertainment, and healthcare sectors. They are employed in a variety of fields, notably text-to-image translation and image processing. Deep learning relies heavily on neural networks, which are also used in pattern analysis and picture identification. The generative models that are discussed here produce data that is similar to the source datasets and is categorized using density estimation. One popular kind of GANs is made up of a discriminator that separates created data from real data and a generator that produces synthetic data[5][[9]. Their adversarial training improves the discriminator’s ability to distinguish between actual and false samples while honing the generator’s capacity to generate realistic data. Neural network activation factors introduce non-linearity; their applicability varies throughout models and tasks. An open-source package called TensorFlow makes GANs and other AI tasks easier[3][15]. Using the MNIST dataset, an experiment demonstrating handwritten digit recognition using TensorFlow and the Rectified Linear Unit (ReLu) activation function is presented. After several epochs, the results show a significant improvement, demonstrating the effectiveness of GANs in picture identification.
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Park, Sung-Wook, Jun-Ho Huh, and Jong-Chan Kim. "BEGAN v3: Avoiding Mode Collapse in GANs Using Variational Inference." Electronics 9, no. 4 (2020): 688. http://dx.doi.org/10.3390/electronics9040688.
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In the field of deep learning, the generative model did not attract much attention until GANs (generative adversarial networks) appeared. In 2014, Google’s Ian Goodfellow proposed a generative model called GANs. GANs use different structures and objective functions from the existing generative model. For example, GANs use two neural networks: a generator that creates a realistic image, and a discriminator that distinguishes whether the input is real or synthetic. If there are no problems in the training process, GANs can generate images that are difficult even for experts to distinguish in terms of authenticity. Currently, GANs are the most researched subject in the field of computer vision, which deals with the technology of image style translation, synthesis, and generation, and various models have been unveiled. The issues raised are also improving one by one. In image synthesis, BEGAN (Boundary Equilibrium Generative Adversarial Network), which outperforms the previously announced GANs, learns the latent space of the image, while balancing the generator and discriminator. Nonetheless, BEGAN also has a mode collapse wherein the generator generates only a few images or a single one. Although BEGAN-CS (Boundary Equilibrium Generative Adversarial Network with Constrained Space), which was improved in terms of loss function, was introduced, it did not solve the mode collapse. The discriminator structure of BEGAN-CS is AE (AutoEncoder), which cannot create a particularly useful or structured latent space. Compression performance is not good either. In this paper, this characteristic of AE is considered to be related to the occurrence of mode collapse. Thus, we used VAE (Variational AutoEncoder), which added statistical techniques to AE. As a result of the experiment, the proposed model did not cause mode collapse but converged to a better state than BEGAN-CS.
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Chai, Kaitian. "The combination of Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) for image super-resolution reconstruction." Advances in Engineering Innovation 16, no. 5 (2025): 95–99. https://doi.org/10.54254/2977-3903/2025.23473.
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This study developed a hybrid model combining a Convolutional Neural Network (CNN) and a Generative Adversarial Network (GAN) for the task of single-image super-resolution reconstruction. The CNN is responsible for hierarchical image feature extraction and maintaining structural integrity, while the GAN synthesizes realistic texture details through an adversarial training mechanism to enhance visual realism. The generator is constructed using densely connected convolutional blocks and is combined with an image block-based discriminator to evaluate the authenticity of local regions. The composite loss function is designed to integrate the root mean square error, perceptual loss, and adversarial loss of the pre-trained GTS network, balancing pixel-level accuracy and visual perceptual effect. Tests on benchmark datasets such as DIV2K and Set14 show that this model outperforms traditional interpolation algorithms and deep learning models in objective indicators such as PSNR and SSIM, as well as in the perception evaluation of LPIPS. Especially in complex texture restoration tasks, the model demonstrates excellent detail restoration capabilities. Experimental data confirm that the adversarial training mechanism effectively solves the common problem of excessive smoothing in traditional super-resolution methods, making the reconstructed image closer to the actual optical imaging effect. This technology provides new ideas for scenarios that require high-fidelity reconstruction, such as medical image analysis and satellite map optimization.
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Toktarbekov, M., and A. Sarsembayev. "AN OVERVIEW OF DEEP LEARNING TECHNIQUES FOR GENERATING MUSICAL COMPOSITIONS." SCIENTIFIC-DISCUSSION, no. 75 (April 10, 2023): 27–34. https://doi.org/10.5281/zenodo.7808643.
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Music generation is increasingly recognized as an attractive field of study in Deep Learning. This paper will focus on a review of the articles that deal with automatic music generation using deep learning methods. A number of well-known architectures, such as Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM), Generative Adversarial Networks (GANs), Variational Auto-Encoders (VAEs). One aspect of Deep Learning that is currently the most popular is music. Artificial intelligence installation is a method of generating digital music using algorithms, neural networks and other performances. Some scientists came to the conclusion that the effectiveness of the models they consider is gradually being optimized, and the melody is positively correlated with musical coherence and creativity. Thus, in this article, scientists’ articles on deep learning methods for creating musical compositions were considered.
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Michelutti, Luca, Alessandro Tel, Marco Zeppieri, et al. "Generative Adversarial Networks (GANs) in the Field of Head and Neck Surgery: Current Evidence and Prospects for the Future—A Systematic Review." Journal of Clinical Medicine 13, no. 12 (2024): 3556. http://dx.doi.org/10.3390/jcm13123556.
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Background: Generative Adversarial Networks (GANs) are a class of artificial neural networks capable of generating content such as images, text, and sound. For several years already, artificial intelligence algorithms have shown promise as tools in the medical field, particularly in oncology. Generative Adversarial Networks (GANs) represent a new frontier of innovation, as they are revolutionizing artificial content generation, opening opportunities in artificial intelligence and deep learning. Purpose: This systematic review aims to investigate what the stage of development of such technology is in the field of head and neck surgery, offering a general overview of the applications of such algorithms, how they work, and the potential limitations to be overcome in the future. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed in conducting this study, and the PICOS framework was used to formulate the research question. The following databases were evaluated: MEDLINE, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), Scopus, ClinicalTrials.gov, ScienceDirect, and CINAHL. Results: Out of 700 studies, only 9 were included. Eight applications of GANs in the head and neck region were summarized, including the classification of craniosynostosis, recognition of the presence of chronic sinusitis, diagnosis of radicular cysts in panoramic X-rays, segmentation of craniomaxillofacial bones, reconstruction of bone defects, removal of metal artifacts from CT scans, prediction of the postoperative face, and improvement of the resolution of panoramic X-rays. Conclusions: Generative Adversarial Networks may represent a new evolutionary step in the study of pathology, oncological and otherwise, making the approach to the disease much more precise and personalized.
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Gupta, Rajat, Rakesh Jindal, Amisha Naik, and K. L. Ganatre. "Roles of CNNs and GANs in Advancing Image Processing Techniques." International Journal of Computer Science and Mobile Computing 14, no. 5 (2025): 15–31. https://doi.org/10.47760/ijcsmc.2025.v14i05.002.
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This research contrasts convolutional neural networks (CNNs) and generative adversarial networks (GANs) in the context of image processing. CNNs have transformed image classification and recognition due to their effectiveness in such tasks. Meanwhile, GANs excel at producing realistic synthetic images, aiding in image enhancement and artistic applications. The study highlights the distinct strengths and challenges of both models in sectors like healthcare, security, and entertainment. Findings indicate that CNNs outperform in classification tasks, while GANs dominate image generation efforts, especially in areas like image inpainting and restoration. Together, CNNs and GANs complement each other by offering distinct solutions for diverse image processing demands.
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Ahmad, Muhammad Nasar, Hariklia D. Skilodimou, Fakhrul Islam, Akib Javed, and George D. Bathrellos. "Drainage Network Generation for Urban Pluvial Flooding (UPF) Using Generative Adversarial Networks (GANs) and GIS Data." Sustainability 17, no. 10 (2025): 4380. https://doi.org/10.3390/su17104380.
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Mapping urban pluvial flooding (UPF) in data-scarce regions poses significant challenges, particularly when drainage systems are inadequate or outdated. These limitations hinder effective flood mitigation and risk assessment. This study proposes an innovative approach to address these challenges by integrating deep learning (DL) models with traditional methods. First, deep convolutional generative adversarial networks (DCGANs) were employed to enhance drainage network data generation. Second, deep recurrent neural networks (DRNNs) and multi-criteria decision analysis (MCDA) methods were implemented to assess UPF. The study compared the performance of these approaches, highlighting the potential of DL models in providing more accurate and robust flood mapping outcomes. The methodology was applied to Lahore, Pakistan—a rapidly urbanizing and data-scarce region frequently impacted by UPF during monsoons. High-resolution ALOS PALSAR DEM data were utilized to extract natural drainage networks, while synthetic datasets generated by GANs addressed the lack of historical flood data. Results demonstrated the superiority of DL-based approaches over traditional MCDA methods, showcasing their potential for broader applicability in similar regions worldwide. This research emphasizes the role of DL models in advancing urban flood mapping, providing valuable insights for urban planners and policymakers to mitigate flooding risks and improve resilience in vulnerable regions.
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Somaini, Antonio. "On the photographic status of images produced by generative adversarial networks (GANs)." Philosophy of Photography 13, no. 1 (2022): 153–64. http://dx.doi.org/10.1386/pop_00044_1.
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The text analyses the new images produced by artificial neural networks such as Generative Adversarial Networks (GANs) from the perspective of photography and, more specifically, cameraless photography. The images produced by GANs are located within the wider framework of the impact of machine learning technologies on contemporary visual culture and contemporary artistic practices. In the final section, the article focuses on the work of two artists who have explicity tackled the relations between GAN-generated images and the traditions of photography and cameraless photography, with their multiple intertwinings of human and non-human agencies: Mario Klingemann and Grégory Chatonsky.
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Noreen, Iram, Muhammad Shahid Muneer, and Saira Gillani. "Deepfake attack prevention using steganography GANs." PeerJ Computer Science 8 (October 20, 2022): e1125. http://dx.doi.org/10.7717/peerj-cs.1125.
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Background Deepfakes are fake images or videos generated by deep learning algorithms. Ongoing progress in deep learning techniques like auto-encoders and generative adversarial networks (GANs) is approaching a level that makes deepfake detection ideally impossible. A deepfake is created by swapping videos, images, or audio with the target, consequently raising digital media threats over the internet. Much work has been done to detect deepfake videos through feature detection using a convolutional neural network (CNN), recurrent neural network (RNN), and spatiotemporal CNN. However, these techniques are not effective in the future due to continuous improvements in GANs. Style GANs can create fake videos with high accuracy that cannot be easily detected. Hence, deepfake prevention is the need of the hour rather than just mere detection. Methods Recently, blockchain-based ownership methods, image tags, and watermarks in video frames have been used to prevent deepfake. However, this process is not fully functional. An image frame could be faked by copying watermarks and reusing them to create a deepfake. In this research, an enhanced modified version of the steganography technique RivaGAN is used to address the issue. The proposed approach encodes watermarks into features of the video frames by training an “attention model” with the ReLU activation function to achieve a fast learning rate. Results The proposed attention-generating approach has been validated with multiple activation functions and learning rates. It achieved 99.7% accuracy in embedding watermarks into the frames of the video. After generating the attention model, the generative adversarial network has trained using DeepFaceLab 2.0 and has tested the prevention of deepfake attacks using watermark embedded videos comprising 8,074 frames from different benchmark datasets. The proposed approach has acquired a 100% success rate in preventing deepfake attacks. Our code is available at https://github.com/shahidmuneer/deepfakes-watermarking-technique.
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V, Sandeep Kumar, Hari Kishore R, Guru Prasadh M, and Divakar R. "ONE SHOT FACE STYLIZATION USING GANS." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem26061.
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One-shot face stylization is an interesting and challenging subject in computer vision and deep learning. This work deals with the art of manipulating a target face using a reference image as inspiration, which requires controlling facial recognition while specifying important style characteristics This project has attracted a lot of interest due to its potential applications in digital art, entertainment, and personal products. In this abstract, we examine the important features of a one- shot face stylization. Deep neural networks, especially generative adversarial networks (GANs), are widely used in the process to generate customized facial images. These networks are trained on data structures that combine the target and reference faces, with the reference image acting as a strategic identifier. The success of a one-shot facial lies in the meticulous execution of the fading process, which strikes a balance between preserving identity and improving technique. These disadvantages typically include a combination of manpower retention, strategic formation, emotional quality, and enemy training. In conclusion, advances in this field have the potential to transform creative expression and personalization across industries from digital art and animation to virtual avatars and social media filters. Key Words: Facial recognition, generative adversarial networks, virtual avatar, image to image transfer.
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Li, Jingtao, Yonglin Shen, and Chao Yang. "An Adversarial Generative Network for Crop Classification from Remote Sensing Timeseries Images." Remote Sensing 13, no. 1 (2020): 65. http://dx.doi.org/10.3390/rs13010065.
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Due to the increasing demand for the monitoring of crop conditions and food production, it is a challenging and meaningful task to identify crops from remote sensing images. The state-of the-art crop classification models are mostly built on supervised classification models such as support vector machines (SVM), convolutional neural networks (CNN), and long- and short-term memory neural networks (LSTM). Meanwhile, as an unsupervised generative model, the adversarial generative network (GAN) is rarely used to complete classification tasks for agricultural applications. In this work, we propose a new method that combines GAN, CNN, and LSTM models to classify crops of corn and soybeans from remote sensing time-series images, in which GAN’s discriminator was used as the final classifier. The method is feasible on the condition that the training samples are small, and it fully takes advantage of spectral, spatial, and phenology features of crops from satellite data. The classification experiments were conducted on crops of corn, soybeans, and others. To verify the effectiveness of the proposed method, comparisons with models of SVM, SegNet, CNN, LSTM, and different combinations were also conducted. The results show that our method achieved the best classification results, with the Kappa coefficient of 0.7933 and overall accuracy of 0.86. Experiments in other study areas also demonstrate the extensibility of the proposed method.
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Bharathi, S., and P. Venkatesan. "Enhanced Classification of Faults of Photovoltaic Module Through Generative Adversarial Network." International Journal of Electrical and Electronics Research 10, no. 3 (2022): 579–84. http://dx.doi.org/10.37391/ijeer.100328.
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The faults occurring in the photo voltaic system has to be detected to make it work efficiently .To detect and classify the faults occurring in the photo voltaic module infrared images, electro luminescent images, photo luminescent images of photo voltaic module is used .Using infrared images around 11 faults of photovoltaic module such as cell ,cell-multi, hot-spot-multi , hot-spot, cracking, diode, diode-multi, vegetation, shadowing, off-line module and soiling faults can be detected. In addition to the original infra-red images (IR) available in the IR dataset, the IR images are generated for each and every category of faults by using generative adversarial networks (GAN’s) to increase the dataset size. 45000 images are generated by GAN’s. Later the images are used to train and test the convolution neural network. The dataset visualization of original and that of GAN generated images are done in 2-dimensional space using uniform manifold approximation and projection. In this work 12 categories of IR dataset are considered for classification in which 11 belongs to fault category and the remaining one is the normal category of images. In earlier work only 11 category of faults or less than that is considered for classification. Compared the results with the existing work and it is found that by enhancing the dataset size by GAN’s accuracy of 91.7 % is obtained during the classification of 8 categories of faults.
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Nishit Agarwal, Venkata Ramanaiah Chintha, Vishesh Narendra Pamadi, Anshika Aggarwal, and Vikhyat Gupta. "GANs for Enhancing Wearable Biosensor Data Accuracy." Universal Research Reports 10, no. 4 (2023): 533–67. http://dx.doi.org/10.36676/urr.v10.i4.1362.
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Wearable biosensors have become indispensable in the realm of health monitoring, providing real-time data on physiological parameters such as heart rate, temperature, and glucose levels. Despite their increasing adoption, these devices often face challenges related to data accuracy, mainly due to sensor noise, signal artifacts, and inconsistencies in sensor quality. Such inaccuracies pose a significant barrier to the reliable use of biosensors in healthcare, reducing their effectiveness for both clinical applications and personal health tracking. To address these limitations, the implementation of Generative Adversarial Networks (GANs) offers a novel and promising solution. GANs consist of two neural networks—the generator and the discriminator—that operate in an adversarial manner. The generator creates synthetic data samples, while the discriminator attempts to distinguish between real and generated data, leading to the continuous refinement of data quality. In the context of wearable biosensors, GANs hold immense potential to improve data accuracy by filtering out noise, correcting signal distortions, and producing high-fidelity synthetic data that mimic real biosensor outputs.
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Zafar, Haroon, Junaid Zafar, and Faisal Sharif. "GANs-Based Intracoronary Optical Coherence Tomography Image Augmentation for Improved Plaques Characterization Using Deep Neural Networks." Optics 4, no. 2 (2023): 288–99. http://dx.doi.org/10.3390/opt4020020.
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Data augmentation using generative adversarial networks (GANs) is vital in the creation of new instances that include imaging modality tasks for improved deep learning classification. In this study, conditional generative adversarial networks (cGANs) were used on a dataset of OCT (Optical Coherence Tomography)-acquired images of coronary atrial plaques for synthetic data creation for the first time, and further validated using deep learning architecture. A new OCT images dataset of 51 patients marked by three professionals was created and programmed. We used cGANs to synthetically populate the coronary aerial plaques dataset by factors of 5×, 10×, 50× and 100× from a limited original dataset to enhance its volume and diversification. The loss functions for the generator and the discriminator were set up to generate perfect aliases. The augmented OCT dataset was then used in the training phase of the leading AlexNet architecture. We used cGANs to create synthetic images and envisaged the impact of the ratio of real data to synthetic data on classification accuracy. We illustrated through experiments that augmenting real images with synthetic images by a factor of 50× during training helped improve the test accuracy of the classification architecture for label prediction by 15.8%. Further, we performed training time assessments against a number of iterations to identify optimum time efficiency. Automated plaques detection was found to be in conformity with clinical results using our proposed class conditioning GAN architecture.
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Jeganathan, Balachandar. "Exploring the Power of Generative Adversarial Networks (GANs) for Image Generation: A Case Study on the MNIST Dataset." International Journal of Advances in Engineering and Management 7, no. 1 (2025): 21–46. https://doi.org/10.35629/5252-07012146.
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This paper provides a comprehensive examination of Generative Adversarial Networks (GANs), a groundbreaking deep learning architecture that has transformed the field of image generation. GANs consist of two neural networks—the generator and the discriminator—that engage in a competitive process to produce highly realistic images by learning patterns from existing data. This study highlights several key applications of GANs, including text-to-image generation, superresolution, neural style transfer, andimage completion. These applications have significant implications for various industries, such as medical imaging, creative content generation, and data augmentation for machinelearning models. The practical implementation section utilizes the MNIST dataset to illustrate how GANs can generate new, realistic images of handwritten digits. This implementation underscores the importance of data preprocessing, such as normalizationandshuffling, in improving model accuracy and performance. The paper also explores the critical role of GPU accelerationin speeding up the training process and the selection of optimal loss functions, particularly the cross-entropy loss, to enhance the discriminator's ability to differentiate between real and generated data. Moreover, the paper addresses the inherent challenges in training GANs, such as mode collapse and the extensive computational resources required. Solutions and best practices, including advanced optimization techniques and checkpointing, are discussed to ensure stable and efficient model training. The results demonstrate the remarkable ability of GANs to generate images that closely resemble real data, with promising applications in the fields of imaging systems, healthcare, and beyond. Future directions for research and emerging trends in GAN technology are also outlined.
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Puttaswamy M R. "Spectral-Spatial Classification of Hyperspectral Imagery Using CNNs and GANs: A Study on Pavia University and Salinas Datasets." Journal of Information Systems Engineering and Management 10, no. 44s (2025): 989–1000. https://doi.org/10.52783/jisem.v10i44s.8696.
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Hyperspectral imaging (HSI) has emerged as a cornerstone of remote sensing, capturing detailed spectral information across hundreds of bands. This study rigorously evaluates the efficacy of deep learning models specifically Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) for HSI classification, using the benchmark Pavia University and Salinas datasets. We address the high dimensionality and limit labelled data challenges inherent in HSI through advanced preprocessing (e.g., noise reduction, normalization) and data augmentation. Our CNN architecture leverages spectral- spatial feature extraction, achieving overall accuracies of 90.25% (Pavia University) and 93.75% (Salinas), surpassing traditional methods. GANs, employed for synthetic data generation, enhance robustness but yield slightly lower accuracies (88.15% and 91.55%, respectively). This work provides a mathematical and empirical foundation for deep learning in HSI, offering insights into model optimization and future research directions.
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Ali, Husnain, Ahmad Aftab, and Saeed Ayesha. "Enhancing agricultural health with AI: Drone-based machine learning for mango tree disease detection." World Journal of Advanced Research and Reviews 23, no. 2 (2024): 1267–76. https://doi.org/10.5281/zenodo.14855478.
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In the agriculture sector, timely detection of diseases in fruit trees is a significant challenge, leading to substantial economic losses. Automated detection of diseases in fruit trees, particularly mango trees, is crucial to minimize these losses by enabling early intervention. This research explores the use of drone-captured multispectral images combined with deep learning and computer vision techniques to detect diseases in mango trees. The proposed system leverages various pre-trained Convolutional Neural Network (CNN) models, such as YOLOv5, Detectron2, and Faster R-CNN, to achieve optimal accuracy. Data augmentation techniques are employed to address data skewness and overfitting, while Generative Adversarial Networks (GANs) enhance image quality. The system aims to provide a scalable solution for early disease detection, thereby reducing economic losses and supporting the agricultural sector's growth.
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Zhou, Kexin. "Advancing Text-to-Image Synthesis with GANs: Integrating CNNs, LSTMs, and Style Transfer Techniques." Applied and Computational Engineering 104, no. 1 (2024): 78–84. http://dx.doi.org/10.54254/2755-2721/104/20241182.
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Abstract. This research pioneers a novel methodology for generating images from text by integrating Generative Adversarial Networks (GANs), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks, effectively bridging the semantic disparity between textual inputs and their visual representations. The proposed model integrates attention mechanisms to enhance semantic precision, making sure that generated images closely align with the provided text. Additionally, style transfer techniques are employed to infuse the images with artistic elements, thereby enriching their visual appeal and diversity. The methodology involves a multi-stage process: CNNs are utilized for feature extraction, LSTMs encode textual descriptions into contextually rich vectors, and style transfer is applied to incorporate artistic styles into the generated images. Extensive experiments demonstrate that the model excels in producing high-fidelity images that not only capture the essence of textual descriptions but also exhibit significant visual diversity. This research makes substantial contributions to the field of GAN-based image synthesis, offering a framework that advances both semantic accuracy and creative expression. The findings provide a solid foundation for future research and innovations in automated image generation, highlighting the potential for further improvements and applications across various domains.
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Al-Milli, Nabeel Refat, and Yazan Alaya Al-Khassawneh. "Intrusion Detection System using CNNs and GANs." WSEAS TRANSACTIONS ON COMPUTER RESEARCH 12 (April 19, 2024): 281–90. http://dx.doi.org/10.37394/232018.2024.12.27.
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This study investigates the effectiveness of deep learning models, namely Generative Adversarial Networks (GANs), Convolutional Neural Networks with three layers (CNN-3L), and Convolutional Neural Networks with four layers (CNN-4L), in the domain of multi-class categorization for intrusion detection. The CICFlowMeter-V3 dataset is utilized to thoroughly evaluate the performance of these models and gain insights into their capabilities. The primary approach involves training the models on the dataset and assessing their accuracy. The GAN achieves an overall accuracy of 93%, while CNN-3L demonstrates a commendable score of 99.71%. Remarkably, CNN-4L excels with a flawless accuracy of 100%. These results underscore the superior performance of CNN-3L and CNN-4L compared to GAN in the context of intrusion detection. Consequently, this study provides valuable insights into the potential of these models and suggests avenues for refining their architectures. The conclusions drawn from this research indicate that CNN-3L and CNN-4L hold promise for enhancing multi-class categorization in intrusion detection systems. It is recommended to further explore these models with diverse datasets to strengthen overall comprehension and practical applicability in this crucial field.
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Peppes, Nikolaos, Theodoros Alexakis, Evgenia Adamopoulou, and Konstantinos Demestichas. "The Effectiveness of Zero-Day Attacks Data Samples Generated via GANs on Deep Learning Classifiers." Sensors 23, no. 2 (2023): 900. http://dx.doi.org/10.3390/s23020900.
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Digitization of most of the services that people use in their everyday life has, among others, led to increased needs for cybersecurity. As digital tools increase day by day and new software and hardware launch out-of-the box, detection of known existing vulnerabilities, or zero-day as they are commonly known, becomes one of the most challenging situations for cybersecurity experts. Zero-day vulnerabilities, which can be found in almost every new launched software and/or hardware, can be exploited instantly by malicious actors with different motives, posing threats for end-users. In this context, this study proposes and describes a holistic methodology starting from the generation of zero-day-type, yet realistic, data in tabular format and concluding to the evaluation of a Neural Network zero-day attacks’ detector which is trained with and without synthetic data. This methodology involves the design and employment of Generative Adversarial Networks (GANs) for synthetically generating a new and larger dataset of zero-day attacks data. The newly generated, by the Zero-Day GAN (ZDGAN), dataset is then used to train and evaluate a Neural Network classifier for zero-day attacks. The results show that the generation of zero-day attacks data in tabular format reaches an equilibrium after about 5000 iterations and produces data that are almost identical to the original data samples. Last but not least, it should be mentioned that the Neural Network model that was trained with the dataset containing the ZDGAN generated samples outperformed the same model when the later was trained with only the original dataset and achieved results of high validation accuracy and minimal validation loss.
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Tanushree, Bharti, Singh Yogam, Jain Mudit, and Kumari Ankita. "Improving Quality of Medical Scans using GANs." International Journal of Innovative Science and Research Technology (IJISRT) 9, no. 12 (2024): 1125–31. https://doi.org/10.5281/zenodo.14557003.
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Improving the quality of medical images is essential for precise diagnosis and treatment planning. When low quality images are used to train the neural network model, the good accuracy cannot be achieved. Nowadays, Generative Adversarial Networks (GANs) have become a potent image enhancement tool that can provide a fresh method for raising the caliber of medical images. In order to improve medical images, this paper presents a GAN-based framework that reduces noise, increases resolution, and corrects artifacts. The suggested technique makes use of a generator network to convert low-quality images into their high-quality equivalents, and a discriminator network to assess the veracity of the improved images. To ensure robustness across various modalities, the model is trained on a diverse dataset of medical images, including MRI, CT, and X-ray scans. Our experimental results show that GAN-based method significantly improves the image quality when compared to conventional methods, as evidenced by enhanced peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) according to quantitative evaluations. This study emphasizes the value of incorporating deep learning methods into medical image processing pipelines and the potential of GANs to advance medical imaging technology so that a robust neural network model can be designed.
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Yannam, Dr Adilakshmi, Mr Eeda Deepak Chandu, Ms Pushpaja kodali, Mr.Sk.Asif, and Mr.G.Moses. "A deep learning method of predicting hourly boarding demand of bus passengers using imbalance records from smart cards." Journal of Nonlinear Analysis and Optimization 16, no. 01 (2025): 1203–10. https://doi.org/10.36893/jnao.2025.v16i01.0141.
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Data from tap-on smart cards is a useful tool for predicting future travel demand and learning about customers' boarding habits. In contrast to negative instances (not boarding at that bus stop at that time), positive instances (i.e., boarding at a specific bus stop at a certain time) are uncommon when looking at the smart-card records (or instances) by boarding stops and by time of day. It has been shown that machine learning algorithms used to forecast hourly boarding numbers from a certain site are far less accurate when the data is unbalanced. Prior to using the smart card data to forecast bus boarding demand, this research resolves the problem of data imbalance. In order to supplement a synthetic training dataset with more evenly distributed travelling and non-traveling cases, we suggest using deep generative adversarial networks (Deep-GAN) to create dummy travelling instances. A deep neural network (DNN) is then trained using the synthetic dataset to forecast the travelling and non-traveling instances from a certain stop within a specified time range. The findings demonstrate that resolving the issue of data imbalance may greatly enhance the prediction model's functionality and better match the true profile of passengers. A comparison of the Deep-GAN's performance with that of other conventional resampling techniques demonstrates that the suggested approach may generate a synthetic training dataset with greater variety and similarity, and consequently, a stronger prediction capability. The importance of enhancing data quality and model performance in travel behaviour prediction and individual travel behaviour analysis is emphasised in the study, along with helpful recommendations.
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Liu, Kanglin, and Guoping Qiu. "Lipschitz constrained GANs via boundedness and continuity." Neural Computing and Applications 32, no. 24 (2020): 18271–83. http://dx.doi.org/10.1007/s00521-020-04954-z.
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AbstractOne of the challenges in the study of generative adversarial networks (GANs) is the difficulty of its performance control. Lipschitz constraint is essential in guaranteeing training stability for GANs. Although heuristic methods such as weight clipping, gradient penalty and spectral normalization have been proposed to enforce Lipschitz constraint, it is still difficult to achieve a solution that is both practically effective and theoretically provably satisfying a Lipschitz constraint. In this paper, we introduce the boundedness and continuity (BC) conditions to enforce the Lipschitz constraint on the discriminator functions of GANs. We prove theoretically that GANs with discriminators meeting the BC conditions satisfy the Lipschitz constraint. We present a practically very effective implementation of a GAN based on a convolutional neural network (CNN) by forcing the CNN to satisfy the BC conditions (BC–GAN). We show that as compared to recent techniques including gradient penalty and spectral normalization, BC–GANs have not only better performances but also lower computational complexity.
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Pinjari Muthu and P. Rohini Bai. "PREDICTING HOURLY BOARDING DEMAND OF BUS PASSENGERS USING IMBALANCED RECORDS FROM SMART-CARDS A DEEP LEARNING APPROACH." International Journal of Engineering Research and Science & Technology 21, no. 2 (2025): 997–1005. https://doi.org/10.62643/10.62643/ijerst.2025.v21.i2.pp997-1005.
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Data from tap-on smart cards is a useful tool for predicting future travel demand and learning about customers' boarding habits. In contrast to negative instances (not boarding at that bus stop at that time), positive instances (i.e., boarding at a particular bus stop at a certain time) are uncommon when looking at the smart-card records (or instances) by boarding stops and by time of day. It has been shown that machine learning algorithms used to forecast hourly boarding numbers from a certain site are far less accurate when the data is unbalanced. Prior to using the smart card data to forecast bus boarding demand, this research resolves the problem of data imbalance. In order to supplement a synthetic training dataset with more evenly distributed travelling and non-traveling cases, we suggest using deep generative adversarial networks (Deep-GAN) to create fake travelling instances. A deep neural network (DNN) is then trained using the synthetic dataset to forecast the travelling and non-traveling instances from a certain stop within a specified time range. The findings demonstrate that resolving the problem of data imbalance may greatly enhance the prediction model's functionality and better match the true profile of passengers. A comparison of the Deep-GAN's performance with that of other conventional resampling techniques demonstrates that the suggested approach may generate a synthetic training dataset with more variety and similarity, and therefore, a better prediction capability. The study emphasises the need of enhancing data quality and model performance for travel behaviour prediction and individual travel behaviour analysis, and it offers helpful advice in this regard.
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Agustin, Tinuk, Indrawan Ady Saputro, and Mochammad Luthfi Rahmadi. "Optimizing Rice Plant Disease Classification Using Data Augmentation with GANs on Convolutional Neural Networks." INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi 9, no. 1 (2025): 97–114. https://doi.org/10.29407/intensif.v9i1.23834.
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Background: Rice disease classification using CNN models faces challenges due to limited data, particularly in minority classes, and inconsistent image quality, which affect model performance. Data augmentation techniques can potentially enhance classification accuracy by improving data diversity and quality. Objective: This study evaluates the effectiveness of data augmentation techniques, specifically classical augmentation and Deep Convolutional Generative Adversarial Networks (DCGAN), in improving CNN performance for rice disease classification. Methods: A quantitative study was conducted using four CNN training scenarios: no augmentation, classical augmentation, DCGAN augmentation, and a combination of both. Model accuracy was analyzed to determine the impact of each augmentation technique. Results: The baseline CNN model achieved an accuracy of 91.88%. Classical augmentation improved accuracy by 2.56%, while DCGAN augmentation led to a 5.44% increase. The combination of classical augmentation and DCGAN yielded the highest accuracy of 98.13%. Conclusion: Data augmentation significantly enhances CNN performance in rice disease classification, with the combined approach of classical augmentation and DCGAN proving to be the most effective. These findings highlight the importance of augmentation techniques in addressing data limitations and improving classification accuracy. Future research should explore additional augmentation strategies and test the model across different datasets to further validate its effectiveness.
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Zhang, Yinbing. "Missing Data Imputation for Health Care Big Data using Denoising Autoencoder with Generative Adversarial Network." Scalable Computing: Practice and Experience 25, no. 5 (2024): 3850–57. http://dx.doi.org/10.12694/scpe.v25i5.3023.
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Missing data imputation is a key topic in healthcare that covers the issues and strategies involved in dealing with partial data in medical records, clinical trials, and health surveys. Data in healthcare might be missing for a variety of reasons, including non-response in surveys, data entry problems, or unrecorded information during therapeutic appointments. This paper introduces a novel approach to impute missing data utilizing a hybrid model that integrates denoising autoencoders with generative adversarial networks (GANs). We begin by highlighting the prevalence of missing data in health care datasets and the potential impact on analytical outcomes. The proposed methodology leverages the denoising autoencoder’s ability to reconstruct data from noisy inputs, coupled with the GAN’s proficiency in generating synthetic data that is indistinguishable from real data. By combining these two neural network architectures, our model demonstrates an enhanced capability to predict and fill in missing data points effectively. To validate our approach, we conducted experiments on several large-scale health care datasets with varying degrees of artificially introduced missingness. The performance of our model was benchmarked against traditional imputation methods such as mean imputation and k-nearest neighbors, as well as against standalone denoising autoencoders and GANs. Our results indicate a significant improvement in imputation accuracy, as measured by root mean square error (RMSE) and mean absolute error (MAE), confirming the efficacy of the hybrid model in handling missing data in a robust manner.
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Hegh, Abya Newton, Adekunle Adedotun Adeyelu, Aamo Iorliam, and Samera U. Otor. "MULTI-MODAL EMOTION RECOGNITION MODEL USING GENERATIVE ADVERSARIAL NETWORKS (GANs) FOR AUGMENTING FACIAL EXPRESSIONS AND PHYSIOLOGICAL SIGNALS." FUDMA JOURNAL OF SCIENCES 9, no. 5 (2025): 277–90. https://doi.org/10.33003/fjs-2025-0905-3412.
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Emotion recognition is a critical area of research with applications in healthcare, human-computer interaction (HCI), security, and entertainment. This study addressed the limitations of single-modal emotion recognition systems by developing a multi-modal emotion recognition model that integrates facial expressions and physiological signals, enhanced by Generative Adversarial Networks (GANs). It aims at improving accuracy, reliability, and robustness in emotion detection, particularly underrepresented emotions. The study utilized the FER-2013 dataset for facial expressions and the DEAP dataset for physiological signals. GANs were employed to augment datasets, address class imbalances and enhance feature diversity. A hybrid multi-modal model was developed, combining Convolutional Neural Networks (CNNs) for facial expression recognition and Long Short-Term Memory (LSTM) networks for physiological signal analysis. Hybrid fusion was used to integrate features at multiple levels, maximizing the complementary strengths of each modality. The results demonstrate significant improvements in emotion recognition. Without GAN augmentation, the CNN and LSTM models achieved accuracies of 62% and 76%, respectively. The hybrid model outperformed, gaining 90% across all metrics. With GAN-augmented datasets, the CNN and LSTM models improved to 81% and 86%, respectively, while the hybrid (multi-modal) model achieved state-of-the-art performance with 93% accuracy and an F1-score of 92%. These findings underscore the efficacy of GANs in enhancing data diversity and the advantages of multi-modal integration for robust emotion recognition. The study contributes to knowledge by introducing a GAN-augmented hybrid multi-modal framework, advancing methodologies in emotion recognition. Recommendations for future work include addressing ethical considerations in emotion recognition systems.
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Adamiak, Maciej, Krzysztof Będkowski, and Anna Majchrowska. "Aerial Imagery Feature Engineering Using Bidirectional Generative Adversarial Networks: A Case Study of the Pilica River Region, Poland." Remote Sensing 13, no. 2 (2021): 306. http://dx.doi.org/10.3390/rs13020306.
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Generative adversarial networks (GANs) are a type of neural network that are characterized by their unique construction and training process. Utilizing the concept of the latent space and exploiting the results of a duel between different GAN components opens up interesting opportunities for computer vision (CV) activities, such as image inpainting, style transfer, or even generative art. GANs have great potential to support aerial and satellite image interpretation activities. Carefully crafting a GAN and applying it to a high-quality dataset can result in nontrivial feature enrichment. In this study, we have designed and tested an unsupervised procedure capable of engineering new features by shifting real orthophotos into the GAN’s underlying latent space. Latent vectors are a low-dimensional representation of the orthophoto patches that hold information about the strength, occurrence, and interaction between spatial features discovered during the network training. Latent vectors were combined with geographical coordinates to bind them to their original location in the orthophoto. In consequence, it was possible to describe the whole research area as a set of latent vectors and perform further spatial analysis not on RGB images but on their lower-dimensional representation. To accomplish this goal, a modified version of the big bidirectional generative adversarial network (BigBiGAN) has been trained on a fine-tailored orthophoto imagery dataset covering the area of the Pilica River region in Poland. Trained models, precisely the generator and encoder, have been utilized during the processes of model quality assurance and feature engineering, respectively. Quality assurance was performed by measuring model reconstruction capabilities and by manually verifying artificial images produced by the generator. The feature engineering use case, on the other hand, has been presented in a real research scenario that involved splitting the orthophoto into a set of patches, encoding the patch set into the GAN latent space, grouping similar patches latent codes by utilizing hierarchical clustering, and producing a segmentation map of the orthophoto.
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Vint, David, Matthew Anderson, Yuhao Yang, Christos Ilioudis, Gaetano Di Caterina, and Carmine Clemente. "Automatic Target Recognition for Low Resolution Foliage Penetrating SAR Images Using CNNs and GANs." Remote Sensing 13, no. 4 (2021): 596. http://dx.doi.org/10.3390/rs13040596.
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In recent years, the technological advances leading to the production of high-resolution Synthetic Aperture Radar (SAR) images has enabled more and more effective target recognition capabilities. However, high spatial resolution is not always achievable, and, for some particular sensing modes, such as Foliage Penetrating Radars, low resolution imaging is often the only option. In this paper, the problem of automatic target recognition in Low Resolution Foliage Penetrating (FOPEN) SAR is addressed through the use of Convolutional Neural Networks (CNNs) able to extract both low and high level features of the imaged targets. Additionally, to address the issue of limited dataset size, Generative Adversarial Networks are used to enlarge the training set. Finally, a Receiver Operating Characteristic (ROC)-based post-classification decision approach is used to reduce classification errors and measure the capability of the classifier to provide a reliable output. The effectiveness of the proposed framework is demonstrated through the use of real SAR FOPEN data.
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Luis, Álvarez Ayuso, and Luis del Blanco García Federico. "Aplicación de redes neuronales al diseño de vivienda colectiva. Procesos generativos de combinatoria y automatización mediante inteligencia artificial = Application of neural networks to the design of collective housing. Automation and combinatorial generative processes using artificial intelligence." rita_ Revista Indexada de Textos Académicos, no. 16 (October 31, 2021): 214–31. https://doi.org/10.24192/2386-7027(2021)(v16)(20).
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El siguiente artículo presenta un trabajo de investigación orientado al uso de inteligencia artificial para el diseño de arquitectura y procesos de automatización. El flujo de trabajo que se plantea hace uso de las capacidades de las redes neuronales en combinación con el diseño de algoritmos de automatización para evitar la repetición de tareas rutinarias. Como prueba de las capacidades de las redes neuronales para su uso en arquitectura, se usa una red neuronal antagónica condicional (Pix2Pix), entrenada por el autor para la generación de planos bidimensionales de vivienda colectiva. Mediante este flujo de trabajo se lleva a cabo la desagregación del conjunto en unidades de vivienda individuales, el procesado por parte de la red neuronal y la re-agregación en el conjunto de viviendas. Debido a la gran cantidad de plantas de vivienda necesarias para el entrenamiento de la red, es necesario automatizar los procesos, realizar un etiquetado y su almacenamiento en listas de datos. Los planos generados por la red neuronal son exportados a Grasshopper para su posterior tratamiento, pudiendo definirse diferentes aproximaciones mediante procesos de automatización. = <em>The following paper presents a research project aiming to use artificial intelligence for architectural design and automation processes. The proposed workflow uses the capabilities of neural networks combined with the design of automation algorithms to avoid the repetition of routine tasks.</em> <em>In order to prove the potential appliance of neural networks in architecture, a conditional adversarial neural network (Pix2Pix) is used and trained by the author for the generation of two-dimensional collective housing floor plans. The workflow includes the dissemination of the collective housing complex into individual units, the processing with the neural network and the re-aggregation back into the assembled group.</em> <em>Due to the large quantity of housing floor plans that are needed for the correct training of the network, it has been necessary to automate its process, tagging and storage in data lists. The plans outputted by the neural network are then exported to Grasshopper, where different approximations can be defined through automation processes </em>
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Qu, Shaoyang. "Advances in Integrating GANs and NeRF for Image Generation and 3D Reconstruction." Transactions on Computer Science and Intelligent Systems Research 7 (November 25, 2024): 370–78. https://doi.org/10.62051/baev1b20.
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This paper investigates recent advancements in integrating Generative Adversarial Networks (GAN) with Neural Radiance Fields (NeRF) for image generation and 3D reconstruction. This integration is pivotal for significantly enhancing the quality of image and 3D scene generation across diverse applications, including medical imaging, virtual reality, animation, and film production. The objective of this study is to summarize and compare existing methods, providing both theoretical and methodological insights. A detailed analysis of various methods is conducted, focusing on their strengths and weaknesses in terms of image quality, multi-view consistency, style migration, and texture transfer. Experimental results across different datasets reveal that while these methods perform effectively within their respective application contexts, which generally face challenges related to high computational resource consumption, training complexity, and the need for extensive multi-view data. Despite these issues, significant progress has been made in enhancing image quality and 3D consistency. This study contributes valuable theoretical insights and offers practical guidance for future research. Future efforts should focus on developing more efficient algorithms, refining training techniques, and exploring applications in medical imaging, virtual reality, animation, and film production to advance image processing and computer vision technologies.
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P, Neethu Prabhakaran, Dini Davis, Priya P.P., Mini Mohan, and Shemitha P.A. "RESIDUAL MULTIHEAD MULTILAYER ATTENTION GANS (RMMLA-GANS) FOR AUTOMATED GLAUCOMA DIAGNOSIS: A NOVEL DEEP LEARNING APPROACH." ICTACT Journal on Image and Video Processing 15, no. 4 (2025): 3606–12. https://doi.org/10.21917/ijivp.2025.0510.
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Glaucoma is a leading cause of irreversible blindness, often diagnosed too late due to subtle symptoms and the reliance on manual evaluation of retinal images. Early and accurate detection is essential for preventing vision loss, yet conventional deep learning methods face challenges in feature generalization and spatial attention. Existing convolutional neural network (CNN)-based and standard GAN approaches often underperform in preserving subtle pathological features and attention mechanisms required for robust glaucoma detection. Moreover, the lack of residual attention integration in multihead architectures limits diagnostic precision. This study proposes a novel deep learning model termed Residual Multihead Multilayer Attention GANs (RMMLA-GANs) that combines the strengths of Generative Adversarial Networks (GANs), residual learning, and multihead attention mechanisms. The generator incorporates multi-layer residual attention blocks and self-attention heads to enhance critical feature localization. A contrastive discriminator improves inter-class feature separability. The model was trained and validated using the RIM-ONE and DRISHTI-GS1 datasets. Our RMMLA-GANs model achieved superior performance over four existing hybrid approaches: Attention U-Net, Dense-GAN, ResNet- GAN, and VGG-GAN. It achieved an accuracy of 96.7%, sensitivity of 97.1%, specificity of 95.4%, AUC of 0.982, and F1-score of 96.3%, outperforming the best existing method by 3.2% in AUC and 2.8% in sensitivity.
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Maack, Lennart, Lennart Holstein, and Alexander Schlaefer. "GANs for generation of synthetic ultrasound images from small datasets." Current Directions in Biomedical Engineering 8, no. 1 (2022): 17–20. http://dx.doi.org/10.1515/cdbme-2022-0005.
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Abstract The task of medical image classification is increasingly supported by algorithms. Deep learning methods like convolutional neural networks (CNNs) show superior performance in medical image analysis but need a high-quality training dataset with a large number of annotated samples. Particularly in the medical domain, the availability of such datasets is rare due to data privacy or the lack of data sharing practices among institutes. Generative adversarial networks (GANs) are able to generate high quality synthetic images. This work investigates the capabilities of different state-of-the-art GAN architectures in generating realistic breast ultrasound images if only a small amount of training data is available. In a second step, these synthetic images are used to augment the real ultrasound image dataset utilized for training CNNs. The training of both GANs and CNNs is conducted with systematically reduced dataset sizes. The GAN architectures are capable of generating realistic ultrasound images. GANs using data augmentation techniques outperform the baseline Style- GAN2 with respect to the Frechet Inception distance by up to 64.2%. CNN models trained with additional synthetic data outperform the baseline CNN model using only real data for training by up to 15.3% with respect to the F1 score, especially for datasets containing less than 100 images. As a conclusion, GANs can successfully be used to generate synthetic ultrasound images of high quality and diversity, improve classification performance of CNNs and thus provide a benefit to computer-aided diagnostics.
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Liu, Minghui, Jiali Deng, Meiyi Yang, et al. "Express Construction for GANs from Latent Representation to Data Distribution." Applied Sciences 12, no. 8 (2022): 3910. http://dx.doi.org/10.3390/app12083910.
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Generative Adversarial Networks (GANs) are powerful generative models for numerous tasks and datasets. However, most of the existing models suffer from mode collapse. The most recent research indicates that the reason for it is that the optimal transportation map from random noise to the data distribution is discontinuous, but deep neural networks (DNNs) can only approximate continuous ones. Instead, the latent representation is a better raw material used to construct a transportation map point to the data distribution than random noise. Because it is a low-dimensional mapping related to the data distribution, the construction procedure seems more like expansion rather than starting all over. Besides, we can also search for more transportation maps in this way with smoother transformation. Thus, we have proposed a new training methodology for GANs in this paper to search for more transportation maps and speed the training up, named Express Construction. The key idea is to train GANs with two independent phases for successively yielding latent representation and data distribution. To this end, an Auto-Encoder is trained to map the real data into the latent space, and two couples of generators and discriminators are used to produce them. To the best of our knowledge, we are the first to decompose the training procedure of GAN models into two more uncomplicated phases, thus tackling the mode collapse problem without much more computational cost. We also provide theoretical steps toward understanding the training dynamics of this procedure and prove assumptions. No extra hyper-parameters have been used in the proposed method, which indicates that Express Construction can be used to train any GAN models. Extensive experiments are conducted to verify the performance of realistic image generation and the resistance to mode collapse. The results show that the proposed method is lightweight, effective, and less prone to mode collapse.
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Singh, Purushottam, Sandip Dutta, and Prashant Pranav. "Optimizing GANs for Cryptography: The Role and Impact of Activation Functions in Neural Layers Assessing the Cryptographic Strength." Applied Sciences 14, no. 6 (2024): 2379. http://dx.doi.org/10.3390/app14062379.
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Generative Adversarial Networks (GANs) have surfaced as a transformative approach in the domain of cryptography, introducing a novel paradigm where two neural networks, the generator (akin to Alice) and the discriminator (akin to Bob), are pitted against each other in a cryptographic setting. A third network, representing Eve, attempts to decipher the encrypted information. The efficacy of this encryption–decryption process is deeply intertwined with the choice of activation functions employed within these networks. This study conducted a comparative analysis of four widely used activation functions within a standardized GAN framework. Our recent explorations underscore the superior performance achieved when utilizing the Rectified Linear Unit (ReLU) in the hidden layers combined with the Sigmoid activation function in the output layer. The non-linear nature introduced by the ReLU provides a sophisticated encryption pattern, rendering the deciphering process for Eve intricate. Simultaneously, the Sigmoid function in the output layer guarantees that the encrypted and decrypted messages are confined within a consistent range, facilitating a straightforward comparison with original messages. The amalgamation of these activation functions not only bolsters the encryption strength but also ensures the fidelity of the decrypted messages. These findings not only shed light on the optimal design considerations for GAN-based cryptographic systems but also underscore the potential of investigating hybrid activation functions for enhanced system optimization. In our exploration of cryptographic strength and training efficiency using various activation functions, we discovered that the “ReLU and Sigmoid” combination significantly outperforms the others, demonstrating superior security and a markedly efficient mean training time of 16.51 s per 2000 steps. This highlights the enduring effectiveness of established methodologies in cryptographic applications. This paper elucidates the implications of these choices, advocating for their adoption in GAN-based cryptographic models, given the superior results they yield in ensuring security and accuracy.
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de Curtò, J., I. de Zarzà, Gemma Roig, and Carlos T. Calafate. "Signature and Log-Signature for the Study of Empirical Distributions Generated with GANs." Electronics 12, no. 10 (2023): 2192. http://dx.doi.org/10.3390/electronics12102192.
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In this paper, we address the research gap in efficiently assessing Generative Adversarial Network (GAN) convergence and goodness of fit by introducing the application of the Signature Transform to measure similarity between image distributions. Specifically, we propose the novel use of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) Signature, along with Log-Signature, as alternatives to existing methods such as Fréchet Inception Distance (FID) and Multi-Scale Structural Similarity Index Measure (MS-SSIM). Our approach offers advantages in terms of efficiency and effectiveness, providing a comprehensive understanding and extensive evaluations of GAN convergence and goodness of fit. Furthermore, we present innovative analytical measures based on statistics by means of Kruskal–Wallis to evaluate the goodness of fit of GAN sample distributions. Unlike existing GAN measures, which are based on deep neural networks and require extensive GPU computations, our approach significantly reduces computation time and is performed on the CPU while maintaining the same level of accuracy. Our results demonstrate the effectiveness of the proposed method in capturing the intrinsic structure of the generated samples, providing meaningful insights into GAN performance. Lastly, we evaluate our approach qualitatively using Principal Component Analysis (PCA) and adaptive t-Distributed Stochastic Neighbor Embedding (t-SNE) for data visualization, illustrating the plausibility of our method.
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Nam, Sungyup, Seungho Jeon, Hongkyo Kim, and Jongsub Moon. "Recurrent GANs Password Cracker For IoT Password Security Enhancement." Sensors 20, no. 11 (2020): 3106. http://dx.doi.org/10.3390/s20113106.
Full textAbstract:
Text-based passwords are a fundamental and popular means of authentication. Password authentication can be simply implemented because it does not require any equipment, unlike biometric authentication, and it relies only on the users’ memory. This reliance on memory is a weakness of passwords, and people therefore usually use easy-to-remember passwords, such as “iloveyou1234”. However, these sample passwords are not difficult to crack. The default passwords of IoT also are text-based passwords and are easy to crack. This weakness enables free password cracking tools such as Hashcat and JtR to execute millions of cracking attempts per second. Finally, this weakness creates a security hole in networks by giving hackers access to an IoT device easily. Research has been conducted to better exploit weak passwords to improve password-cracking performance. The Markov model and probabilistic context-free-grammar (PCFG) are representative research results, and PassGAN, which uses generative adversarial networks (GANs), was recently introduced. These advanced password cracking techniques contribute to the development of better password strength checkers. We studied some methods of improving the performance of PassGAN, and developed two approaches for better password cracking: the first was changing the convolutional neural network (CNN)-based improved Wasserstein GAN (IWGAN) cost function to an RNN-based cost function; the second was employing the dual-discriminator GAN structure. In the password cracking performance experiments, our models showed 10–15% better performance than PassGAN. Through additional performance experiments with PCFG, we identified the cracking performance advantages of PassGAN and our models over PCFG. Finally, we prove that our models enhanced password strength estimation through a comparison with zxcvbn.
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Hung, Shih-Kai, and John Q. Gan. "Small facial image dataset augmentation using conditional GANs based on incomplete edge feature input." PeerJ Computer Science 7 (November 17, 2021): e760. http://dx.doi.org/10.7717/peerj-cs.760.
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Image data collection and labelling is costly or difficult in many real applications. Generating diverse and controllable images using conditional generative adversarial networks (GANs) for data augmentation from a small dataset is promising but challenging as deep convolutional neural networks need a large training dataset to achieve reasonable performance in general. However, unlabeled and incomplete features (e.g., unintegral edges, simplified lines, hand-drawn sketches, discontinuous geometry shapes, etc.) can be conveniently obtained through pre-processing the training images and can be used for image data augmentation. This paper proposes a conditional GAN framework for facial image augmentation using a very small training dataset and incomplete or modified edge features as conditional input for diversity. The proposed method defines a new domain or space for refining interim images to prevent overfitting caused by using a very small training dataset and enhance the tolerance of distortions caused by incomplete edge features, which effectively improves the quality of facial image augmentation with diversity. Experimental results have shown that the proposed method can generate high-quality images of good diversity when the GANs are trained using very sparse edges and a small number of training samples. Compared to the state-of-the-art edge-to-image translation methods that directly convert sparse edges to images, when using a small training dataset, the proposed conditional GAN framework can generate facial images with desirable diversity and acceptable distortions for dataset augmentation and significantly outperform the existing methods in terms of the quality of synthesised images, evaluated by Fréchet Inception Distance (FID) and Kernel Inception Distance (KID) scores.
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Brian Limantoro, Darius Mulyadi Putra, Nicholas Marco Weinandra, et al. "Generative Adversarial Networks (GANs) 2D imaging optimization in image–J for estimation of endodontic filling material measurement: A review." World Journal of Biology Pharmacy and Health Sciences 20, no. 2 (2024): 228–33. http://dx.doi.org/10.30574/wjbphs.2024.20.2.0854.
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Background: Root canal treatment (RCT) remains the most common clinical intervention in dentistry, treating a wide range of pulp-related conditions and structural tooth injuries. RCT begins by partially removing the crown to access and remove the infected pulp through a meticulous process of cleaning, reshaping, and irrigating the canal. This prepared canal is filled with gutta-percha as the endodontic filling material (EFM), and adhesive cement is used to seal the canal before final restoration with a crown. The success of RCT, however, hinges on accurately measuring and applying the EFM volume within the canal. Current measurement practices using 2D radiographic imaging fall short in accuracy due to operator limitations and the inherent lack of depth and clarity in 2D images, which contribute to high variability in EFM application. CBCT has emerged as a viable alternative, offering comprehensive 3D imaging for a more accurate EFM volume assessment, yet its high radiation levels, cost, and accessibility issues pose challenges. Thus, with increasing demand for safer and cost-effective alternatives, AI-driven enhancements in 2D imaging are being explored to achieve CBCT-level accuracy without its drawbacks, marking a significant step forward in RCT reliability and success rates. Purpose: To explore the potential of GANs and Image–J integration on 2D images for estimation of EFM measurement. Method: A literature review of studies published within the past five years from sources like ResearchGate, ScienceDirect, PubMed, and Google Scholar. Result: This study explores the integration of artificial intelligence, specifically convolutional neural networks (CNN) and generative adversarial networks (GANs), to enhance the accuracy of endodontic filling material (EFM) measurements in root canal treatments (RCT). CNN's structure, comprising multilayered networks with pooling layers, has shown potential in medical imaging by segmenting anatomical details for EFM measurement. However, challenges remain due to CNN's reliance on extensive datasets and its susceptibility to misinterpretation when faced with complex anatomical variations. GANs, combining a generator and discriminator, address these limitations by enabling unsupervised learning, allowing the generation of photorealistic data samples for more precise EFM measurement. This bidirectional learning system refines measurement accuracy through continuous feedback between the generator and discriminator, achieving superior outcomes across parameters like radiopacity, dimensional consistency, classification performance, and sealing ability. Furthermore, the AI model is enhanced by integrating Image-J, a Java-based image analysis software with precise 2D measurement capabilities. Despite its effectiveness in analyzing length and volume, Image-J has limitations due to its dependence on the operator’s interpretation, especially with low-quality 2D images. The combined use of GANs with Image-J allows for automated, detailed border detection of the pulp anatomy, improving measurement accuracy even with complex root canal forms. The results suggest that this integrated AI approach can advance clinical practices by providing precise, rapid, and consistent EFM measurements, potentially overcoming the limitations of traditional 2D radiographic methods and addressing CBCT’s drawbacks in radiation exposure and accessibility. Conclusion: The integration of generative adversarial networks (GANs) with Image-J offers a promising advancement in the measurement of endodontic filling materials (EFM) for root canal treatments based on 2D imaging. GANs enhance Image-J’s measurement precision and accuracy by utilizing bidirectional deep learning, which enables continuous learning and refinement without requiring extensive external inputs. This approach not only improves EFM estimation but also presents a viable alternative to cone-beam computed tomography (CBCT), reducing potential health risks and economic burdens associated with CBCT. The combined GANs and Image-J system thus supports a more accessible, efficient, and precise methodology for EFM assessment in clinical dentistry.
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Srinivasan, Srinitish, Varenya Pathak, Abirami Sasinthiran, Sherly Alphonse, and Sakthivel Gnanasekaran. "Enhancing colour and brush stroke pattern recognition for stable abstract art generation using modified deep convolutional GANs." Intelligent Decision Technologies 19, no. 2 (2024): 594–610. https://doi.org/10.1177/18724981241298513.
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Abstract Art, a highly popular artistic genre, often serves as a canvas for expressing the artist's emotions. Numerous researchers have endeavoured to analyse abstract art through the application of machine and deep learning techniques, focusing on tasks such as edge detection, brushstroke analysis, and emotion recognition. This research paper presents an investigation of a wide distribution of abstract paintings using Generative Adversarial Neural-Networks(GANs). GANs have the ability to learn and reproduce a distribution enabling researchers and scientists to effectively explore and study the generated image space. However, the challenge lies in developing an efficient GAN architecture that overcomes common training pitfalls. This paper addresses this challenge by introducing a modified-DCGAN(mDCGAN) specifically designed for high-quality artwork generation. The proposed mDCGAN incorporates meticulous adjustments in layer configurations, offering tailored optimisation techniques and loss functions to effectively combat issues like mode collapse and gradient vanishing in order to improve stability and realism in art generation. The evaluation results of mDCGAN demonstrates a remarkable reduction in mode collapse occurrences when compared to the standard DCGAN configuration. Further this paper explores the generated latent space by performing random walks to understand vector relationships between brush strokes and colours in the abstract art space and a statistical analysis of unstable outputs after a certain period of GAN training and compare its significant difference. These findings validate the effectiveness of the proposed approach, emphasising its potential to revolutionise the field of digital art generation and digital art ecosystem.
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Ruan, Diwang, Xinzhou Song, Clemens Gühmann, and Jianping Yan. "Collaborative Optimization of CNN and GAN for Bearing Fault Diagnosis under Unbalanced Datasets." Lubricants 9, no. 10 (2021): 105. http://dx.doi.org/10.3390/lubricants9100105.
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Convolutional Neural Network (CNN) has been widely used in bearing fault diagnosis in recent years, and many satisfying results have been reported. However, when the training dataset provided is unbalanced, such as the samples in some fault labels are very limited, the CNN’s performance reduces inevitably. To solve the dataset imbalance problem, a Generative Adversarial Network (GAN) has been preferably adopted for the data generation. In published research studies, GAN only focuses on the overall similarity of generated data to the original measurement. The similarity in the fault characteristics is ignored, which carries more information for the fault diagnosis. To bridge this gap, this paper proposes two modifications for the general GAN. Firstly, a CNN, together with a GAN, and two networks are optimized collaboratively. The GAN provides a more balanced dataset for the CNN, and the CNN outputs the fault diagnosis result as a correction term in the GAN generator’s loss function to improve the GAN’s performance. Secondly, the similarity of the envelope spectrum between the generated data and the original measurement is considered. The envelope spectrum error from the 1st to 5th order of the Fault Characteristic Frequencies (FCF) is taken as another correction in the GAN generator’s loss function. Experimental results show that the bearing fault samples generated by the optimized GAN contain more fault information than the samples produced by the general GAN. Furthermore, after the data augmentation for the unbalanced training sets, the CNN’s accuracy in the fault classification has been significantly improved.
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Joseph, Nnaemeka Chukwunweike, Yussuf Moshood, Okusi Oluwatobiloba, Oluwatobi Bakare Temitope, and J. Abisola Ayokunle. "The role of deep learning in ensuring privacy integrity and security: Applications in AI-driven cybersecurity solutions." World Journal of Advanced Research and Reviews 23, no. 2 (2024): 1778–90. https://doi.org/10.5281/zenodo.14865286.
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This article explores the critical role of deep learning in developing AI-driven cybersecurity solutions, with a particular focus on privacy integrity and information security. It investigates how deep neural networks (DNNs) and advanced machine learning techniques are being used to detect and neutralize cyber threats in real time. The article also considers the implications of these technologies for data privacy, discussing the potential risks and benefits of using AI to protect sensitive information. By examining case studies and current research, the piece provides insights into how organizations can deploy deep learning models to enhance both security and privacy integrity in a digital world.