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Journal articles on the topic 'Deep Convolutional Generative Adversarial Networks (DCGAN)'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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1

Manimegala M, Gokulraj V, Karisni K, and Manisha S. "Generating Human Face with Dcgan and Gan." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 05 (2024): 1348–54. http://dx.doi.org/10.47392/irjaeh.2024.0186.

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Generative Adversarial Networks (GANs) are prominent in unsupervised learning for their exceptional data-generation capabilities. GANs utilize backpropagation and a competitive process between a Generative Network (G) and a Discriminative Network (D). In this setup, G generates artificial images while D distinguishes real from artificial ones, enhancing G's ability to create realistic images. Deep Convolutional Generative Adversarial Networks (DCGAN) are particularly notable, using a convolutional architecture to produce high-quality human face images. This study trains DCGAN on the CelebFaces

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Tang, Kejia. "Emojis Generation Based on Deep Convolution Generative Adversarial Network." Applied and Computational Engineering 8, no. 1 (2023): 203–9. http://dx.doi.org/10.54254/2755-2721/8/20230126.

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With the development of information technology and mobile communication, people's usage of emoji is increasing. However, designing emojis by artists can be time-consuming and costly. Therefore, this study attempts to use the Deep Convolution Generative Adversarial Network (DCGAN) method in deep learning to automatically generate emojis. DCGAN is a combination of Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN). It introduces convolutional networks into the generative model for unsupervised training, which can improve the learning effect of the generator network. DCGA

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Kumar, Dheeraj, Mayuri A. Mehta, and Indranath Chatterjee. "Empirical Analysis of Deep Convolutional Generative Adversarial Network for Ultrasound Image Synthesis." Open Biomedical Engineering Journal 15, no. 1 (2021): 71–77. http://dx.doi.org/10.2174/1874120702115010071.

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Introduction: Recent research on Generative Adversarial Networks (GANs) in the biomedical field has proven the effectiveness in generating synthetic images of different modalities. Ultrasound imaging is one of the primary imaging modalities for diagnosis in the medical domain. In this paper, we present an empirical analysis of the state-of-the-art Deep Convolutional Generative Adversarial Network (DCGAN) for generating synthetic ultrasound images. Aims: This work aims to explore the utilization of deep convolutional generative adversarial networks for the synthesis of ultrasound images and to

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Xing, Jin. "Exploiting Deep Convolutional Generative Adversarial Network Generated Images for Enhanced Image Classification." Transactions on Computer Science and Intelligent Systems Research 5 (August 12, 2024): 476–81. http://dx.doi.org/10.62051/vq4pyb84.

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The power of deep neural networks relies heavily on the quantity and quality of training data. However, it is expensive and time consuming for people to collect and annotate data on a large scale. Traditional methods, including modifying the copies of existing data, do not always have the effect, especially in some biomedical fields where some large-size anonymous datasets are generally not publicly available. So, this paper tried to tackle this problem by generating specific data using Deep Convolutional Generative Adversarial Network (DCGAN). DCGAN structure combines convolution and traditio

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Fujioka, Tomoyuki, Mio Mori, Kazunori Kubota, et al. "Breast Ultrasound Image Synthesis using Deep Convolutional Generative Adversarial Networks." Diagnostics 9, no. 4 (2019): 176. http://dx.doi.org/10.3390/diagnostics9040176.

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Deep convolutional generative adversarial networks (DCGANs) are newly developed tools for generating synthesized images. To determine the clinical utility of synthesized images, we generated breast ultrasound images and assessed their quality and clinical value. After retrospectively collecting 528 images of 144 benign masses and 529 images of 216 malignant masses in the breasts, synthesized images were generated using a DCGAN with 50, 100, 200, 500, and 1000 epochs. The synthesized (n = 20) and original (n = 40) images were evaluated by two radiologists, who scored them for overall quality, d

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Thakur, Amey. "Generative Adversarial Networks." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (2021): 2307–25. http://dx.doi.org/10.22214/ijraset.2021.37723.

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Abstract: Deep learning's breakthrough in the field of artificial intelligence has resulted in the creation of a slew of deep learning models. One of these is the Generative Adversarial Network, which has only recently emerged. The goal of GAN is to use unsupervised learning to analyse the distribution of data and create more accurate results. The GAN allows the learning of deep representations in the absence of substantial labelled training information. Computer vision, language and video processing, and image synthesis are just a few of the applications that might benefit from these represen

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Xiang, Wenjie, Zhongchang Song, Xuming Peng, et al. "The development of deep convolutional generative adversarial network to synthesize odontocetes' clicks." Journal of the Acoustical Society of America 157, no. 1 (2025): 328–39. https://doi.org/10.1121/10.0034865.

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Odontocetes are capable of dynamically changing their echolocation clicks to efficiently detect targets, and learning their clicking strategy can facilitate the design of man-made detecting signals. In this study, we developed deep convolutional generative adversarial networks guided by an acoustic feature vector (AF-DCGANs) to synthesize narrowband clicks of the finless porpoise (Neophocaena phocaenoides sunameri) and broadband clicks of the bottlenose dolphins (Tursiops truncatus). The average short-time objective intelligibility (STOI), spectral correlation coefficient (Spe-CORR), waveform

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Huo, Yu. "Generate handwritten images based on DCGAN." Applied and Computational Engineering 4, no. 1 (2023): 165–70. http://dx.doi.org/10.54254/2755-2721/4/20230438.

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Nowadays, more and more machines are applied to artificial intelligence, and generate something itself has become more and more available. To augment databases to get a more accurate result from training a model, generating data is necessary. This paper introduces how to generate new handwritten images by training the existing database. The Generative Adversarial Networks model is used as the basic model. Deep Convolutional Generative Adversarial Networks and the MINIST database are applied as the method and training dataset respectively in this research. After 50 epochs by training 256 images

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Liu, Yukai. "Data augmentation-based enhanced fingerprint recognition using deep convolutional generative adversarial network and diffusion models." Applied and Computational Engineering 52, no. 1 (2024): 8–13. http://dx.doi.org/10.54254/2755-2721/52/20241115.

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The progress of fingerprint recognition applications encounters substantial hurdles due to privacy and security concerns, leading to limited fingerprint data availability and stringent data quality requirements. This article endeavors to tackle the challenges of data scarcity and data quality in fingerprint recognition by implementing data augmentation techniques. Specifically, this research employed two state-of-the-art generative models in the domain of deep learning, namely Deep Convolutional Generative Adversarial Network (DCGAN) and the Diffusion model, for fingerprint data augmentation.

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Sharma, Moolchand, Prerna Sharma, Manish Kumar Jha, and Rohan Singh. "MOTION TRANSFER IN VIDEOS USING DCGAN." Innovative Computing and Communication: An International Journal 2, no. 1 (2020): 17–24. https://doi.org/10.5281/zenodo.4743820.

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Motion Transfer has a wide variety of applications, such as creating motion synchronized videos in film industries and video making apps. The research paper presents a novel approach for motion transfer from a source video to the target person. This approach focuses on the video to video translation using various poses generated in the frames of video for translation. The approach makes use of Pose Generation Convolutional Neural Network to synthesize arbitrary poses from source videos and train the pix2pix – DCGAN(Deep Convolutional Generative Adversarial Networks), which is a condition

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Liu, Lu, and Guobao Feng. "Polarimetric SAR image classification using 3D generative adversarial network." MATEC Web of Conferences 336 (2021): 08012. http://dx.doi.org/10.1051/matecconf/202133608012.

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In this paper, a new architecture of three-dimensional deep convolutional generative adversarial network(3D-DCGAN) is specially defined to solve the unstable training problem of GAN and make full use of the information involved in polarimetric data. Firstly, a data cube with nine components of polarimetric coherency matrix are directly used as the input features of DCGAN. After that, a 3D convolutional model is designed as the components of generator and discriminator to construct the 3D-DCGAN, which considers the effective feature extraction capability of 3D convolutional neural network(CNN).

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Wang, Huidong, Yurun Ma, Aihua Zhang, Dongmei Lin, Yusheng Qi, and Jiaqi Li. "Deep Convolutional Generative Adversarial Network with LSTM for ECG Denoising." Computational and Mathematical Methods in Medicine 2023 (February 10, 2023): 1–17. http://dx.doi.org/10.1155/2023/6737102.

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The electrocardiogram (ECG), as an essential basis for the diagnosis of cardiovascular diseases, is usually disturbed by various noise. To obtain accurate human physiological information from ECG, the denoising and reconstruction of ECG are critical. In this paper, we proposed an ECG denoising method referred to as LSTM-DCGAN which is based on an improved generative adversarial network (GAN). The overall network structure is composed of multiple layers of convolutional networks. Furthermore, the convolutional features can be connected to their time series order dependence by adding LSTM layers

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Gao, Yangde, Farzin Piltan, and Jong-Myon Kim. "A Novel Image-Based Diagnosis Method Using Improved DCGAN for Rotating Machinery." Sensors 22, no. 19 (2022): 7534. http://dx.doi.org/10.3390/s22197534.

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Rotating machinery plays an important role in industrial systems, and faults in the machinery may damage the system health. A novel image-based diagnosis method using improved deep convolutional generative adversarial networks (DCGAN) is proposed for the feature recognition and fault classification of rotating machinery. First, vibration signal data from the rotating machinery is transformed into time–frequency feature 2-D image data by a continuous wavelet transform and used for fault classification with the neural network method. The adaptive deep convolution neural network (ADCNN) is then c

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Kang, Zhiqiao. "Exploring the Effectiveness of Hyperparameters in Deep Convolution Generative Adversarial Networks." Transactions on Computer Science and Intelligent Systems Research 5 (August 12, 2024): 178–88. http://dx.doi.org/10.62051/h3sxs218.

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Traditional machine learning models are often only able to classify or predict data, and cannot generate new simulated data, while Generative Adversarial Network (GAN) provides the possibility for machines to generate high-quality and diverse data. GAN can be used for image data generation, image-to-image transformation, translation of image information and text information into each other, and so on. There are many different types of GAN can realize different types of functions. GAN has a strong generating ability, can generate some high-quality simulation data for human reference. The framew

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Adhithya, Raguram, I. P. Venkatesh, and S. Srividhya Dr. "Automatic Image Colorization using Generative Adversarial Networks." Advancement in Image Processing and Pattern Recognition 5, no. 2 (2022): 1–5. https://doi.org/10.5281/zenodo.6758133.

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Image colorization is an approach of transforming a black and white image into colorized image. The colonization process can also be used to perform color corrections. This application has been incorporated  in large software like Adobe Photoshop, After Effects and Lightroom, and Da Vinci Resolve to aid users through their editing process. In the past, the process of colorization required a tremendous amount of human involvement and the results were still not properly saturated. <em>The approach considered for this topic is a fully generalized procedure using</em> <em>a conditional Deep C

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Yi, Fan. "Investigation of generative capacity related to DCGANs across varied discriminator architectures and parameter counts: A comparative study." Applied and Computational Engineering 52, no. 1 (2024): 1–7. http://dx.doi.org/10.54254/2755-2721/52/20241111.

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Generating lifelike images through generative models poses a significant challenge, where Generative Adversarial Networks (GANs), particularly Deep Convolutional GANs (DCGANs), are commonly employed for image synthesis. This study focuses on altering the DCGAN discriminators structure and parameter count, investigating their effects on the characteristics of the resulting generated images. Assessment of these models is carried out using the Frchet Inception Distance (FID) score, a metric that gauges the quality of generated image samples. The research specifically involves substituting some co

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Liu, Bingqi, Jiwei Lv, Xinyue Fan, Jie Luo, and Tianyi Zou. "Application of an Improved DCGAN for Image Generation." Mobile Information Systems 2022 (July 18, 2022): 1–14. http://dx.doi.org/10.1155/2022/9005552.

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With the rapid development of deep learning, image generation technology has become one of the current hot research areas. A deep convolutional generative adversarial network (DCGAN) can better adapt to complex image distributions than other methods. In this paper, based on a traditional generative adversarial networks (GANs) image generation model, first, the fully connected layer of the DCGAN is further improved. To solve the problem of gradient disappearance in GANs, the activation functions of all layers of the discriminator are LeakyReLU functions, the output layer of the generator uses t

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Yuwana, R. Sandra, Fani Fauziah, Ana Heryana, Dikdik Krisnandi, R. Budiarianto Suryo Kusumo, and Hilman F. Pardede. "Data Augmentation using Adversarial Networks for Tea Diseases Detection." Jurnal Elektronika dan Telekomunikasi 20, no. 1 (2020): 29. http://dx.doi.org/10.14203/jet.v20.29-35.

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Deep learning technology has a better result when trained using an abundant amount of data. However, collecting such data is expensive and time consuming. On the other hand, limited data often be the inevitable condition. To increase the number of data, data augmentation is usually implemented. By using it, the original data are transformed, by rotating, shifting, or both, to generate new data artificially. In this paper, generative adversarial networks (GAN) and deep convolutional GAN (DCGAN) are used for data augmentation. Both approaches are applied for diseases detection. The performance o

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Stephen Lui, Michael, Fitra Abdurrachman Bachtiar, and Novanto Yudistira. "Penerapan Deep Convolutional Generative Adversarial Network Untuk Menciptakan Data Sintesis Perilaku Pengemudi Dalam Berkendara." Jurnal Teknologi Informasi dan Ilmu Komputer 10, no. 5 (2023): 963–72. http://dx.doi.org/10.25126/jtiik.20231056978.

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Kecelakaan kendaraan adalah salah satu penyebab kematian tertinggi di Indonesia. Salah satu solusi untuk mencegah kecelakaan adalah dengan menggunakan sensor eksternal untuk mendeteksi kondisi jalan. Namun, penyebab utama kecelakaan adalah kelalaian pengemudi ketika mengemudi yang tidak dapat terdeteksi oleh sensor eksternal. Sensor visual dapat mendeteksi perilaku pengemudi di dalam kendaraan. Penggunaan sensor visual memiliki performa yang lebih baik ketika menggunakan metode deep learning. Salah satu metode untuk meningkatkan performa metode deep learning adalah dengan menggunakan data sint

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Stephen Lui, Michael, Fitra Abdurrachman Bachtiar, and Novanto Yudistira. "Penerapan Deep Convolutional Generative Adversarial Network Untuk Menciptakan Data Sintesis Perilaku Pengemudi Dalam Berkendara." Jurnal Teknologi Informasi dan Ilmu Komputer 10, no. 5 (2023): 963–72. https://doi.org/10.25126/jtiik.2023106978.

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Kecelakaan kendaraan adalah salah satu penyebab kematian tertinggi di Indonesia. Salah satu solusi untuk mencegah kecelakaan adalah dengan menggunakan sensor eksternal untuk mendeteksi kondisi jalan. Namun, penyebab utama kecelakaan adalah kelalaian pengemudi ketika mengemudi yang tidak dapat terdeteksi oleh sensor eksternal. Sensor visual dapat mendeteksi perilaku pengemudi di dalam kendaraan. Penggunaan sensor visual memiliki performa yang lebih baik ketika menggunakan metode deep learning. Salah satu metode untuk meningkatkan performa metode deep learning adalah dengan menggunakan data sint

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Ma, Yunjiao. "Lung Nodule Data Enhancement Algorithm Based on Generative Adversarial Network." Academic Journal of Science and Technology 7, no. 2 (2023): 183–86. http://dx.doi.org/10.54097/ajst.v7i2.12267.

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Due to the strong privacy of data and the difficulty of annotation, the amount of medical image data is relatively small, which further affects the effect of deep learning. Using data enhancement technology to expand existing data sets can significantly alleviate the problem of insufficient data. Deep Convolutional generative adversarial Network (DCGAN) technology has been widely used in the field of medical image data enhancement, but there are still some problems such as unstable training, difficulty in convergence, easy to produce mode collapse and insufficient quality of the generated imag

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Dewi, Christine, Rung-Ching Chen, Yan-Ting Liu, and Hui Yu. "Various Generative Adversarial Networks Model for Synthetic Prohibitory Sign Image Generation." Applied Sciences 11, no. 7 (2021): 2913. http://dx.doi.org/10.3390/app11072913.

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A synthetic image is a critical issue for computer vision. Traffic sign images synthesized from standard models are commonly used to build computer recognition algorithms for acquiring more knowledge on various and low-cost research issues. Convolutional Neural Network (CNN) achieves excellent detection and recognition of traffic signs with sufficient annotated training data. The consistency of the entire vision system is dependent on neural networks. However, locating traffic sign datasets from most countries in the world is complicated. This work uses various generative adversarial networks

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Rajarajeswari, Perepi, Redrowthu Vijaya Saraswathi, Yelamanchi Jahnavi, et al. "Performance evaluation of generative adversarial networks for anime face synthesis using deep learning approaches." Multidisciplinary Science Journal 7, no. 3 (2024): 2025093. http://dx.doi.org/10.31893/multiscience.2025093.

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Anime characters have transcended their traditional animation role, permeating entertainment, video games, and social media. Online platforms, including prominent blogs and forums, are filled with references and adaptations of these characters. This digital ubiquity has spurred creators to develop innovative storytelling methods through the internet, fostering new avenues for anime character creation. Notably, advanced systems like Generative Adversarial Networks (GANs) have emerged as powerful tools for this purpose. This research investigates the capabilities of various GAN variants in gener

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Tong, Yuanxin, Hongxia Luo, Zili Qin, Hua Xia, and Xinyao Zhou. "Enhanced Landslide Susceptibility Assessment in Western Sichuan Utilizing DCGAN-Generated Samples." Land 14, no. 1 (2024): 34. https://doi.org/10.3390/land14010034.

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The scarcity of landslide samples poses a critical challenge, impeding the broad application of machine learning techniques in landslide susceptibility assessment (LSA). To address this issue, this study introduces a novel approach leveraging a deep convolutional generative adversarial network (DCGAN) for data augmentation aimed at enhancing the efficacy of various machine learning methods in LSA, including support vector machines (SVMs), convolutional neural networks (CNNs), and residual neural networks (ResNets). Experimental results present substantial enhancements across all three models,

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Alrumiah, Sarah S., Norah Alrebdi, and Dina M. Ibrahim. "Augmenting healthy brain magnetic resonance images using generative adversarial networks." PeerJ Computer Science 9 (April 11, 2023): e1318. http://dx.doi.org/10.7717/peerj-cs.1318.

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Machine learning applications in the medical sector face a lack of medical data due to privacy issues. For instance, brain tumor image-based classification suffers from the lack of brain images. The lack of such images produces some classification problems, i.e., class imbalance issues which can cause a bias toward one class over the others. This study aims to solve the imbalance problem of the “no tumor” class in the publicly available brain magnetic resonance imaging (MRI) dataset. Generative adversarial network (GAN)-based augmentation techniques were used to solve the imbalance classificat

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Jia, Nan, Xiaolin Tian, Wenxing Gao, and Licheng Jiao. "Deep Graph-Convolutional Generative Adversarial Network for Semi-Supervised Learning on Graphs." Remote Sensing 15, no. 12 (2023): 3172. http://dx.doi.org/10.3390/rs15123172.

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Graph convolutional networks (GCNs) are neural network frameworks for machine learning on graphs. They can simultaneously perform end-to-end learning on the attribute information and the structure information of graph data. However, most existing GCNs inevitably encounter the limitations of non-robustness and low classification accuracy when labeled nodes are scarce. To address the two issues, the deep graph convolutional generative adversarial network (DGCGAN), a model combining GCN and deep convolutional generative adversarial networks (DCGAN), is proposed in this paper. First, the graph dat

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Mourad, Drici, and Kazeem Oluwakemi Oseni. "Synthetic Brain Images: Bridging the Gap in Brain Mapping With Generative Adversarial Model." International Journal of Managing Information Technology 16, no. 1 (2024): 01–14. http://dx.doi.org/10.5121/ijmit.2024.16101.

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Magnetic Resonance Imaging (MRI) is a vital modality for gaining precise anatomical information, and it plays a significant role in medical imaging for diagnosis and therapy planning. Image synthesis problems have seen a revolution in recent years due to the introduction of deep learning techniques, specifically Generative Adversarial Networks (GANs). This work investigates the use of Deep Convolutional Generative Adversarial Networks (DCGAN) for producing high-fidelity and realistic MRI image slices. The suggested approach uses a dataset with a variety of brain MRI scans to train a DCGAN arch

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Gao, Chenxi. "Generative Adversarial Networks-based solution for improving medical data quality and insufficiency." Applied and Computational Engineering 49, no. 1 (2024): 167–75. http://dx.doi.org/10.54254/2755-2721/49/20241086.

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As big data brings intelligent solutions and innovations to various fields, the goal of this research is to solve the problem of poor-quality and insufficient datasets in the medical field, to help poor areas can access to high quality and rich medical datasets as well. This study focuses on solving the current problem by utilizing variants of generative adversarial network, Super Resolution Generative Adversarial Network (SRGAN) and Deep Convolutional Generative Adversarial Network (DCGAN). In this study, OpenCV is employed to introduce fuzziness to the Brain Tumor MRI Dataset, resulting in a

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Hou, Guisheng, Shuo Xu, Nan Zhou, Lei Yang, and Quanhao Fu. "Remaining Useful Life Estimation Using Deep Convolutional Generative Adversarial Networks Based on an Autoencoder Scheme." Computational Intelligence and Neuroscience 2020 (August 1, 2020): 1–14. http://dx.doi.org/10.1155/2020/9601389.

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Accurate predictions of remaining useful life (RUL) of important components play a crucial role in system reliability, which is the basis of prognostics and health management (PHM). This paper proposed an integrated deep learning approach for RUL prediction of a turbofan engine by integrating an autoencoder (AE) with a deep convolutional generative adversarial network (DCGAN). In the pretraining stage, the reconstructed data of the AE not only participate in its error reconstruction but also take part in the DCGAN parameter training as the generated data of the DCGAN. Through double-error reco

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Et. al., Dr V. Vijeya Kaveri ,. "Image Generation for Real Time Application Using DCGAN (Deep Convolutional Generative Adversarial Neural Network)." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 11 (2021): 617–21. http://dx.doi.org/10.17762/turcomat.v12i11.5936.

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As the technology keeps developing the unimaginable possibilities keep happening. And it leads to easy use of our daily life. In image processing when the CNNs came to our life it makes the world to turn around and makes the human work easier in all organization. Convolutional Neural Network were mainly used in computer vision, mainly in face recognition, image classification, action recognition, and document analysis, but these gets difficult when comes to dataset. Gathering dataset for machine learning is time consuming operation, at that point the new technique called GAN were introduced. I

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Hu, Yuzhi. "Convolutional neural network for classifying cartoon images augmented by DCGAN." Theoretical and Natural Science 19, no. 1 (2023): 70–75. http://dx.doi.org/10.54254/2753-8818/19/20230498.

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Convolutional Neural Network (CNN) tend to have better results on large data sets and poor performance on small data sets, so the data augmentation is crucial for a CNN to get better performance based on the dataset with limited size. In this paper, Deep Convolution Generative Adversarial Network (DCGAN) was used to augment data to make the AlexNet perform better on an image classification task with small data sets. AlexNet was trained on a small anime face training set with only 160 samples to determine whether the anime face was male or female, and then tested its accuracy on a test set with

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Yang, Yujie. "Exploiting Feature Space Manipulation for Image Generation in Deep Convolutional Generative Adversarial Networks." Transactions on Computer Science and Intelligent Systems Research 5 (August 12, 2024): 256–64. http://dx.doi.org/10.62051/v2j19752.

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Image generation and manipulation are important research directions in the field of computer vision and graphics. Traditional methods of image generation and manipulation often necessitate substantial manual intervention or are constrained by the quality and diversity of the dataset, leading to a lack of authenticity and control in the images produced. This paper provides an extensive examination of image generation and manipulation using Deep Convolutional Generative Adversarial Networks (DCGAN) and feature weighting operations. The model successfully captured the basic structure of a face. F

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Zhang, Leihong, Weihong Lin, Zimin Shen, et al. "Infrared Dim and Small Target Sequence Dataset Generation Method Based on Generative Adversarial Networks." Electronics 12, no. 17 (2023): 3625. http://dx.doi.org/10.3390/electronics12173625.

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With the development of infrared technology, infrared dim and small target detection plays a vital role in precision guidance applications. To address the problems of insufficient dataset coverage and huge actual shooting costs in infrared dim and small target detection methods, this paper proposes a method for generating infrared dim and small target sequence datasets based on generative adversarial networks (GANs). Specifically, first, the improved deep convolutional generative adversarial network (DCGAN) model is used to generate clear images of the infrared sky background. Then, target–bac

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Sreedhar, P. S. S. S., Tedla Balaji, and Somayajulu Meduri Sai. "Implementation of image fusion model using DCGAN." i-manager’s Journal on Image Processing 9, no. 4 (2022): 35. http://dx.doi.org/10.26634/jip.9.4.19229.

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Remote Sensing Images (RSI) are captured by the satellites. The quality of the RSIs primarily depends on environmental conditions and image-capturing device capability. Rapid development in technology leads to the generation of High- Resolution (HR) images from satellites. However, these images are to be processed in a scientific way for the best results. A new Image Fusion (IF) technique with the help of wavelets, Deep Convolutional Generative Adversarial Networks (DCGAN), was designed to get super-resolution images for satellite images. Residual Convolution Neural Network (ResNet) increases

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Shan, Yibing, Lei Xiao, and Baiteng Ma. "A remaining useful life prediction method based on LSTM-DCGAN for aero-engines." Journal of Physics: Conference Series 2591, no. 1 (2023): 012063. http://dx.doi.org/10.1088/1742-6596/2591/1/012063.

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Abstract Turbofan engine is a key component in aerospace. Its health condition determines whether an aircraft can operate reliably. However, it is difficult to predict the remaining useful life (RUL) precisely because of the characteristics of complex operating conditions and various failure modes. To predict the RUL more accurately and make full use of the advantages of neural networks, a RUL prediction model based on a long short-term memory network (LSTM) and deep convolutional generative adversarial network (DCGAN) is proposed and called LSTM-DCGAN in this paper. In the proposed LSTM-DCGAN

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Zia, Rabbia, Mariam Rehman, Afzaal Hussain, Shahbaz Nazeer, and Maria Anjum. "Improving synthetic media generation and detection using generative adversarial networks." PeerJ Computer Science 10 (September 20, 2024): e2181. http://dx.doi.org/10.7717/peerj-cs.2181.

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Synthetic images are created using computer graphics modeling and artificial intelligence techniques, referred to as deepfakes. They modify human features by using generative models and deep learning algorithms, posing risks violations of social media regulations and spread false information. To address these concerns, the study proposed an improved generative adversarial network (GAN) model which improves accuracy while differentiating between real and fake images focusing on data augmentation and label smoothing strategies for GAN training. The study utilizes a dataset containing human fa

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Lv, Luhui, Jiarula Yasenjiang, Debo Wang, and Lihua Xu. "Fault diagnosis method for rolling bearings with small samples based on DCGAN and improved swin transformer." Journal of Physics: Conference Series 2954, no. 1 (2025): 012113. https://doi.org/10.1088/1742-6596/2954/1/012113.

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Abstract In industrial production, it is difficult for us to obtain a large amount of fault data. To solve the problem of fault diagnosis with a small number of samples, we suggest a fault diagnosis method based on Deep Convolutional Generative Adversarial Networks (DCGAN) and the improved Swin transformer. This method uses DCGAN to expand fault samples and combine Swin Transformer to classify large images, achieving the recognition of fault states. From the experimental results, the method effectively addresses the challenges posed by uneven data, improving model generalization and fault diag

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Liu, Xinhua, Yao Zou, Chengjuan Xie, Hailan Kuang, and Xiaolin Ma. "Bidirectional Face Aging Synthesis Based on Improved Deep Convolutional Generative Adversarial Networks." Information 10, no. 2 (2019): 69. http://dx.doi.org/10.3390/info10020069.

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The use of computers to simulate facial aging or rejuvenation has long been a hot research topic in the field of computer vision, and this technology can be applied in many fields, such as customs security, public places, and business entertainment. With the rapid increase in computing speeds, complex neural network algorithms can be implemented in an acceptable amount of time. In this paper, an optimized face-aging method based on a Deep Convolutional Generative Adversarial Network (DCGAN) is proposed. In this method, an original face image is initially mapped to a personal latent vector by a

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Rao, K. Nitalaksheswara, P. Jayasree, Ch V. Murali Krishna, K. Sai Prasanth, and Ch Satyananda Reddy. "Image Anonymization using Deep Convolutional Generative Adversarial Network." Journal of Physics: Conference Series 2089, no. 1 (2021): 012012. http://dx.doi.org/10.1088/1742-6596/2089/1/012012.

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Abstract Advancement in deep learning requires significantly huge amount of data for training purpose, where protection of individual data plays a key role in data privacy and publication. Recent developments in deep learning demonstarte a huge challenge for traditionally used approch for image anonymization, such as model inversion attack, where adversary repeatedly query the model, inorder to reconstrut the original image from the anonymized image. In order to apply more protection on image anonymization, an approach is presented here to convert the input (raw) image into a new synthetic ima

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Berezsky, Oleh M., and Petro B. Liashchynskyi. "Comparison of generative adversarial networks architectures for biomedical images synthesis." Applied Aspects of Information Technology 4, no. 3 (2021): 250–60. http://dx.doi.org/10.15276/aait.03.2021.4.

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The article analyzes and compares the architectures of generativeadversarialnetworks. These networks are based on convolu-tional neural networks that are widely used for classification problems. Convolutional networks require a lot of training data to achieve the desired accuracy. Generativeadversarialnetworks are used for the synthesis of biomedical images in this work. Biomedi-cal images are widely used in medicine, especially in oncology. For diagnosis in oncology biomedical images are divided into three classes: cytological, histological, and immunohistochemical. Initial samples of biomedi

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Qaderi, Soran, Abbas Maghsoudi, Amin Beiranvand Pour, Abdorrahman Rajabi, and Mahyar Yousefi. "DCGAN-Based Feature Augmentation: A Novel Approach for Efficient Mineralization Prediction Through Data Generation." Minerals 15, no. 1 (2025): 71. https://doi.org/10.3390/min15010071.

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This study aims to improve the efficiency of mineral exploration by introducing a novel application of Deep Convolutional Generative Adversarial Networks (DCGANs) to augment geological evidence layers. By training a DCGAN model with existing geological, geochemical, and remote sensing data, we have synthesized new, plausible layers of evidence that reveal unrecognized patterns and correlations. This approach deepens the understanding of the controlling factors in the formation of mineral deposits. The implications of this research are significant and could improve the efficiency and success ra

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Guo, Baoqing, Gan Geng, Liqiang Zhu, Hongmei Shi, and Zujun Yu. "High-Speed Railway Intruding Object Image Generating with Generative Adversarial Networks." Sensors 19, no. 14 (2019): 3075. http://dx.doi.org/10.3390/s19143075.

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Foreign object intrusion is a great threat to high-speed railway safety operations. Accurate foreign object intrusion detection is particularly important. As a result of the lack of intruding foreign object samples during the operational period, artificially generated ones will greatly benefit the development of the detection methods. In this paper, we propose a novel method to generate railway intruding object images based on an improved conditional deep convolutional generative adversarial network (C-DCGAN). It consists of a generator and multi-scale discriminators. Loss function is also imp

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Yin, Hang, Yurong Wei, Hedan Liu, Shuangyin Liu, Chuanyun Liu, and Yacui Gao. "Deep Convolutional Generative Adversarial Network and Convolutional Neural Network for Smoke Detection." Complexity 2020 (November 12, 2020): 1–12. http://dx.doi.org/10.1155/2020/6843869.

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Real-time smoke detection is of great significance for early warning of fire, which can avoid the serious loss caused by fire. Detecting smoke in actual scenes is still a challenging task due to large variance of smoke color, texture, and shapes. Moreover, the smoke detection in the actual scene is faced with the difficulties in data collection and insufficient smoke datasets, and the smoke morphology is susceptible to environmental influences. To improve the performance of smoke detection and solve the problem of too few datasets in real scenes, this paper proposes a model that combines a dee

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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

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Kora Venu, Sagar, and Sridhar Ravula. "Evaluation of Deep Convolutional Generative Adversarial Networks for Data Augmentation of Chest X-ray Images." Future Internet 13, no. 1 (2020): 8. http://dx.doi.org/10.3390/fi13010008.

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Medical image datasets are usually imbalanced due to the high costs of obtaining the data and time-consuming annotations. Training a deep neural network model on such datasets to accurately classify the medical condition does not yield the desired results as they often over-fit the majority class samples’ data. Data augmentation is often performed on the training data to address the issue by position augmentation techniques such as scaling, cropping, flipping, padding, rotation, translation, affine transformation, and color augmentation techniques such as brightness, contrast, saturation, and

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Behara, Kavita, Ernest Bhero, and John Terhile Agee. "Skin Lesion Synthesis and Classification Using an Improved DCGAN Classifier." Diagnostics 13, no. 16 (2023): 2635. http://dx.doi.org/10.3390/diagnostics13162635.

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The prognosis for patients with skin cancer improves with regular screening and checkups. Unfortunately, many people with skin cancer do not receive a diagnosis until the disease has advanced beyond the point of effective therapy. Early detection is critical, and automated diagnostic technologies like dermoscopy, an imaging device that detects skin lesions early in the disease, are a driving factor. The lack of annotated data and class-imbalance datasets makes using automated diagnostic methods challenging for skin lesion classification. In recent years, deep learning models have performed wel

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Sachar, Silky, and Anuj Kumar. "DCGAN-based deep learning approach for medicinal leaf identification." Journal of Information and Optimization Sciences 44, no. 4 (2023): 717–23. http://dx.doi.org/10.47974/jios-1270.

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Though visual identification of plants seems easier for the trained botanists or agriculturists, the automated identification of plants using leaf images still remains a challenging task. The proper identification of plants forms the most important phase as it leads to usage of plants for various purposes. In this paper, we have manually collected about 30 leaves per species belonging to five medicinal plant species. The dataset was created using the scans of the adaxial and abaxial sides of the leaves. As the small number of images makes it difficult for the Convolutional neural network to le

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K, Prabhakar, Umaselvi M, Shibili Said, and Saswata Das. "DEMENTIA DISEASE CLASSIFICATION WITH ROTATION FORESTS BASED DCGAN." ICTACT Journal on Image and Video Processing 14, no. 1 (2023): 3055–59. https://doi.org/10.21917/ijivp.2023.0434.

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This research paper introduces a novel approach for the classification of dementia disease using Rotation Forests based on Deep Convolutional Generative Adversarial Networks (DCGAN). Dementia is a significant cognitive disorder prevalent among the elderly population, demanding accurate and early diagnosis for effective intervention. Traditional methods often rely on manual feature extraction and shallow learning, which may lack the ability to capture intricate patterns in complex medical data. In this study, we propose a fusion of Rotation Forests, a robust ensemble learning technique, with DC

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Long, Xinyue, and Mingchuan Zhang. "An Overview of Generative Adversarial Networks." Journal of Computing and Electronic Information Management 10, no. 3 (2023): 31–36. http://dx.doi.org/10.54097/jceim.v10i3.8677.

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Generative adversarial network (GAN), put forward by two-person zero-sum game theory, is one of the most important research hotspots in the field of artificial intelligence. With a generator network and a discriminator network, GAN is trained by adversarial learning. In this paper, we aim to discusses the development status of GAN. We first introduce the basic idea and training process of GAN in detail, and summarize the structure and structure of GAN derivative models, including conditional GAN, deep convolution DCGAN, WGAN based on Wasserstein distance and WGAN-GP based on gradient strategy.

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Machado, Jorge, Ana Marta, Pedro Mestre, João Melo Beirão, and António Cunha. "Data Augmentation with Generative Methods for Inherited Retinal Diseases: A Systematic Review." Applied Sciences 15, no. 6 (2025): 3084. https://doi.org/10.3390/app15063084.

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Inherited retinal diseases (IRDs) are rare and genetically diverse disorders that cause progressive vision loss and affect 1 in 3000 individuals worldwide. Their rarity and genetic variability pose a challenge for deep learning models due to the limited amount of data. Generative models offer a promising solution by creating synthetic data to improve training datasets. This study carried out a systematic literature review to investigate the use of generative models to augment data in IRDs and assess their impact on the performance of classifiers for these diseases. Following PRISMA 2020 guidel

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