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Journal articles on the topic 'Generative adversarial model'

Author: Grafiati

Published: 22 February 2023

Last updated: 31 July 2025

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1

Huo, Lin, Huanchao Qi, Simiao Fei, Cong Guan, and Ji Li. "A Generative Adversarial Network Based a Rolling Bearing Data Generation Method Towards Fault Diagnosis." Computational Intelligence and Neuroscience 2022 (July 13, 2022): 1–21. http://dx.doi.org/10.1155/2022/7592258.

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As a new generative model, the generative adversarial network (GAN) has great potential in the accuracy and efficiency of generating pseudoreal data. Nowadays, bearing fault diagnosis based on machine learning usually needs sufficient data. If enough near-real data can be generated in the case of insufficient samples in the actual operating condition, the effect of fault diagnosis will be greatly improved. In this study, a new rolling bearing data generation method based on the generative adversarial network (GAN) is proposed, which can be trained adversarially and jointly via a learned embedd

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Dingeto, Hiskias, and Juntae Kim. "Universal Adversarial Training Using Auxiliary Conditional Generative Model-Based Adversarial Attack Generation." Applied Sciences 13, no. 15 (2023): 8830. http://dx.doi.org/10.3390/app13158830.

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While Machine Learning has become the holy grail of modern-day computing, it has many security flaws that have yet to be addressed and resolved. Adversarial attacks are one of these security flaws, in which an attacker appends noise to data samples that machine learning models take as input with the aim of fooling the model. Various adversarial training methods have been proposed that augment adversarial examples in the training dataset for defense against such attacks. However, a general limitation exists where a robust model can only protect itself against adversarial attacks that are known

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Iranmanesh, Seyed Mehdi, and Nasser M. Nasrabadi. "HGAN: Hybrid generative adversarial network." Journal of Intelligent & Fuzzy Systems 40, no. 5 (2021): 8927–38. http://dx.doi.org/10.3233/jifs-201202.

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In this paper, we present a simple approach to train Generative Adversarial Networks (GANs) in order to avoid a mode collapse issue. Implicit models such as GANs tend to generate better samples compared to explicit models that are trained on tractable data likelihood. However, GANs overlook the explicit data density characteristics which leads to undesirable quantitative evaluations and mode collapse. To bridge this gap, we propose a hybrid generative adversarial network (HGAN) for which we can enforce data density estimation via an autoregressive model and support both adversarial and likelih

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Deupa, Abhishek, Garima Saha, Nikhil S. Sharma, and Virendra Pal Singh. "Generative Adversarial Network Based Music Generation." Far Western Review 2, no. 1 (2024): 1–25. http://dx.doi.org/10.3126/fwr.v2i1.70491.

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Music has been an integral part of human civilization personally and culturally. Historically, music has been generated using various instruments, or natural sounds like water drops, or unconventional musical instruments like metal or glass-wares. At present, technologies like Musical Instrument Digital Interface (MIDI) are used to generate music electronically. This research investigates the use of Generative Adversarial Networks (GANs) for beginner-friendly music production. This model uses Long Short-Term Memory (LSTM) generator and Patch GAN as discriminator for the GAN architecture. The g

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Yin, Haiyan, Dingcheng Li, Xu Li, and Ping Li. "Meta-CoTGAN: A Meta Cooperative Training Paradigm for Improving Adversarial Text Generation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 9466–73. http://dx.doi.org/10.1609/aaai.v34i05.6490.

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Training generative models that can generate high-quality text with sufficient diversity is an important open problem for Natural Language Generation (NLG) community. Recently, generative adversarial models have been applied extensively on text generation tasks, where the adversarially trained generators alleviate the exposure bias experienced by conventional maximum likelihood approaches and result in promising generation quality. However, due to the notorious defect of mode collapse for adversarial training, the adversarially trained generators face a quality-diversity trade-off, i.e., the g

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Imamverdiyev, Yadigar, and Firangiz Musayeva. "Analysis of generative adversarial networks." Problems of Information Technology 13, no. 1 (2022): 20–27. http://dx.doi.org/10.25045/jpit.v13.i1.03.

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Recently, a lot of research has been done on the use of generative models in the field of computer vision and image classification. At the same time, effective work has been done with the help of an environment called generative adversarial networks, such as video generation, music generation, image synthesis, text-to-image conversion. Generative adversarial networks are artificial intelligence algorithms designed to solve the problems of generative models. The purpose of the generative model is to study the set of training patterns and their probable distribution. The article discusses genera

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Babu, Niby, Varghese S. Chooralil, Jucy Vareed, and Hrudya K.P. "ETHICS AND FAIRNESS IN GENERATIVE AI USING MITIGATING BIAS IN LARGE LANGUAGE MODELS USING ADVERSARIAL TRAINING." ICTACT Journal on Soft Computing 15, no. 3 (2025): 3598–607. https://doi.org/10.21917/ijsc.2025.0500.

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Generative AI has revolutionized natural language processing (NLP) by enabling the creation of coherent and contextually relevant text. However, these models are susceptible to biases embedded in training datasets, leading to ethical concerns about fairness and equitable representation. This problem becomes critical in applications such as recruitment, healthcare, and education, where biased decisions can exacerbate social inequalities. Addressing these challenges requires robust methodologies to detect and mitigate bias in large language models. This study explores adversarial training as a m

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Rohith, Vallabhaneni. "Evaluating Transferability of Attacks across Generative Models." Engineering and Technology Journal 9, no. 06 (2024): 4261–67. https://doi.org/10.5281/zenodo.12223027.

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The need for adversarial sample transferability is to attack black-box deep learning models. Whereas much recent work focuses on making untargeted adversarial attacks more transferable, there has been scarce research on the creation of transferable targeted adversarial instances that can trick models into believing they are of a particular class. The present transferable targeted adversarial attacks are not transferable since they cannot sufficiently define the distribution of target classes. In this paper, we propose a generative adversarial training system consisting of a feature-label dual

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Kumar, Krishna, Hardwari Lal Mandoria, Rajeev Singh, Shri Prakash Dwivedi, and Paras N. "Malware Detection and Classification using Generative Adversarial Network." International Journal of Computer Science and Information Technology 16, no. 5 (2024): 93–110. http://dx.doi.org/10.5121/ijcsit.2024.16508.

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The Generative Adversarial Networks (GANs) are playing a crucial role in deep-learning-based malware classification to overcome the dataset imbalance and unseen malware. The Generative AI is preferably used in many applications, such as improving image resolution and generating audio, video, and text. The cybercriminals are also using the Generative AI for generating the malware and deepfake videos to harm the targeted person or device. By generating the synthetic data, it makes the deep learning model more robust to detect such types of unseen and adversarial attacks. This work utilizes GANs

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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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Wang, Yunpeng, Meng Pang, Shengbo Chen, and Hong Rao. "Consistency-GAN: Training GANs with Consistency Model." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 14 (2024): 15743–51. http://dx.doi.org/10.1609/aaai.v38i14.29503.

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For generative learning tasks, there are three crucial criteria for generating samples from the models: quality, coverage/diversity, and sampling speed. Among the existing generative models, Generative adversarial networks (GANs) and diffusion models demonstrate outstanding quality performance while suffering from notable limitations. GANs can generate high-quality results and enable fast sampling, their drawbacks, however, lie in the limited diversity of the generated samples. On the other hand, diffusion models excel at generating high-quality results with a commendable diversity. Yet, its i

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Chi, Wanle, Yun Huoy Choo, and Ong Sing Goh. "Review of Generative Adversarial Networks in Image Generation." Journal of Advanced Computational Intelligence and Intelligent Informatics 26, no. 1 (2022): 3–7. http://dx.doi.org/10.20965/jaciii.2022.p0003.

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Generative adversarial network (GAN) model generates and discriminates images using an adversarial competitive strategy to generate high-quality images. The implementation of GAN in different fields is helpful for generating samples that are not easy to obtain. Image generation can help machine learning to balance data and improve the accuracy of the classifier. This paper introduces the principles of the GAN model and analyzes the advantages and disadvantages of improving GANs. The applications of GANs in image generation are analyzed. Finally, the problems of GANs in image generation are sum

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Lai, Peter, and Feruza Amirkulova. "Acoustic metamaterial design using Conditional Wasserstein Generative Adversarial Networks." Journal of the Acoustical Society of America 151, no. 4 (2022): A253. http://dx.doi.org/10.1121/10.0011234.

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This talk presents a method for generating planar configurations of scatterers with a reduced total scattering cross section (TSCS) by means of generative modeling and deep learning. The TSCS minimization via repeated forward modeling techniques, trial-error methods, and traditional optimization methods requires considerable computer resources and time. However, similar or better results can be achieved more efficiently by training a deep learning model to generate such optimized configurations producing low scattering effect. In this work, the Conditional Wasserstein Generative Adversarial Ne

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Chang, Yeong-Hwa, Pei-Hua Chung, Yu-Hsiang Chai, and Hung-Wei Lin. "Color Face Image Generation with Improved Generative Adversarial Networks." Electronics 13, no. 7 (2024): 1205. http://dx.doi.org/10.3390/electronics13071205.

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This paper focuses on the development of an improved Generative Adversarial Network (GAN) specifically designed for generating color portraits from sketches. The construction of the system involves using a GPU (Graphics Processing Unit) computing host as the primary unit for model training. The tasks that require high-performance calculations are handed over to the GPU host, while the user host only needs to perform simple image processing and use the model trained by the GPU host to generate images. This arrangement reduces the computer specification requirements for the user. This paper will

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Phan, Huy, Yi Xie, Siyu Liao, Jie Chen, and Bo Yuan. "CAG: A Real-Time Low-Cost Enhanced-Robustness High-Transferability Content-Aware Adversarial Attack Generator." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5412–19. http://dx.doi.org/10.1609/aaai.v34i04.5990.

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Deep neural networks (DNNs) are vulnerable to adversarial attack despite their tremendous success in many artificial intelligence fields. Adversarial attack is a method that causes the intended misclassfication by adding imperceptible perturbations to legitimate inputs. To date, researchers have developed numerous types of adversarial attack methods. However, from the perspective of practical deployment, these methods suffer from several drawbacks such as long attack generating time, high memory cost, insufficient robustness and low transferability. To address the drawbacks, we propose a Conte

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Xie, Yi, Zhuohang Li, Cong Shi, Jian Liu, Yingying Chen, and Bo Yuan. "Enabling Fast and Universal Audio Adversarial Attack Using Generative Model." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (2021): 14129–37. http://dx.doi.org/10.1609/aaai.v35i16.17663.

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Recently, the vulnerability of deep neural network (DNN)-based audio systems to adversarial attacks has obtained increasing attention. However, the existing audio adversarial attacks allow the adversary to possess the entire user's audio input as well as granting sufficient time budget to generate the adversarial perturbations. These idealized assumptions, however, make the existing audio adversarial attacks mostly impossible to be launched in a timely fashion in practice (e.g., playing unnoticeable adversarial perturbations along with user's streaming input). To overcome these limitations, in

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Huang, Wenqi, Lingyu Liang, Zhen Dai, et al. "Scenario Reduction of Power Systems with Renewable Generations Using Improved Time-GAN." Journal of Physics: Conference Series 2662, no. 1 (2023): 012009. http://dx.doi.org/10.1088/1742-6596/2662/1/012009.

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Abstract To investigate the uncertainties and spatiotemporal complexities of renewable energy represented by wind and photovoltaic, a scenario reduction of power systems with renewable generations uses improved time series generative adversarial networks (Time GAN). The long short-term memory neural network is used to construct the generative adversarial networks, and the time-series supervision loss function and generative adversarial loss function are introduced to jointly optimize the generator network for better generating the daily renewable energy power scenarios. Based on the results of

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Yang, LianShuQing, and Jiahui Li. "Research on the Creation of Chinese National Cultural Identity Symbols Based on Visual Images." Mathematical Problems in Engineering 2022 (September 13, 2022): 1–9. http://dx.doi.org/10.1155/2022/8848307.

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Cultural symbol generation has always been a challenging task to achieve symbols that can represent national culture and promote people’s identification with Chinese culture. In this paper, we combine generative adversarial network (GAN) to propose a symbolic generation model of Chinese national cultural identity based on visual images. First, combining pattern search regular terms with generator cross-loss functions based on GAN generative adversarial networks to improve the pattern collapse phenomenon of generative adversarial networks. Second, the normal convolutional layer of the generator

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Chen, Yize, Yishen Wang, Daniel Kirschen, and Baosen Zhang. "Model-Free Renewable Scenario Generation Using Generative Adversarial Networks." IEEE Transactions on Power Systems 33, no. 3 (2018): 3265–75. http://dx.doi.org/10.1109/tpwrs.2018.2794541.

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Lin, Zhiqun, and Kexin Feng. "Improved generative adversarial networks model for movie dance generation." PLOS One 20, no. 5 (2025): e0323304. https://doi.org/10.1371/journal.pone.0323304.

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To address the challenges of innovation and efficiency in film choreography, this study proposes a dance generation model based on the generative adversarial networks. The model is trained using the AIST++ dance motion dataset, incorporating data from multiple dance styles to ensure that the generated dance sequences accurately mimic various stylistic and technical characteristics. The model integrates a music synchronization mechanism and dance structure constraints. These features ensure that the generated dance aligns seamlessly with the background music in terms of rhythm and emotional exp

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Naman and Sudha Narang, Chaudhary Sarimurrab, Ankita Kesari. "Human Face Generation using Deep Convolution Generative Adversarial Network." January 2021 7, no. 01 (2021): 114–20. http://dx.doi.org/10.46501/ijmtst070127.

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The Generative Models have gained considerable attention in the field of unsupervised learning via a new and practical framework called Generative Adversarial Networks (GAN) due to its outstanding data generation capability. Many models of GAN have proposed, and several practical applications emerged in various domains of computer vision and machine learning. Despite GAN's excellent success, there are still obstacles to stable training. In this model, we aim to generate human faces through un-labelled data via the help of Deep Convolutional Generative Adversarial Networks. The applications for

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Dandekar, Anushree, Rohini Malladi, Payal Gore, and Dr Vipul Dalal. "Text to Image Synthesis using Generative Adversarial Networks." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 2723–30. http://dx.doi.org/10.22214/ijraset.2023.50584.

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Abstract: Image generation has been a significant field of research in computer vision and machine learning for several years. It involves generating new images that resemble real-world images based on a given input or set of inputs. This process has a wide range of applications, including video games, computer graphics, and image editing. With the advancements in deep learning, the development of generative models has revolutionized the field of image generation. Generative models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) have demonstrated remarkable s

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Li, Xu, Bowei Li, Minghao Fang, Rui Huang, and Xiaoran Huang. "BaMSGAN: Self-Attention Generative Adversarial Network with Blur and Memory for Anime Face Generation." Mathematics 11, no. 20 (2023): 4401. http://dx.doi.org/10.3390/math11204401.

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In this paper, we propose a novel network, self-attention generative adversarial network with blur and memory (BaMSGAN), for generating anime faces with improved clarity and faster convergence while retaining the capacity for continuous learning. Traditional self-attention generative adversarial networks (SAGANs) produce anime faces of higher quality compared to deep convolutional generative adversarial networks (DCGANs); however, some edges remain blurry and distorted, and the generation speed is sluggish. Additionally, common issues hinder the model’s ability to learn continuously. To addres

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Liu, Decheng, Xijun Wang, Chunlei Peng, Nannan Wang, Ruimin Hu, and Xinbo Gao. "Adv-Diffusion: Imperceptible Adversarial Face Identity Attack via Latent Diffusion Model." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 4 (2024): 3585–93. http://dx.doi.org/10.1609/aaai.v38i4.28147.

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Adversarial attacks involve adding perturbations to the source image to cause misclassification by the target model, which demonstrates the potential of attacking face recognition models. Existing adversarial face image generation methods still can’t achieve satisfactory performance because of low transferability and high detectability. In this paper, we propose a unified framework Adv-Diffusion that can generate imperceptible adversarial identity perturbations in the latent space but not the raw pixel space, which utilizes strong inpainting capabilities of the latent diffusion model to genera

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Hah, Junghoon, Woojin Lee, Jaewook Lee, and Saerom Park. "Information-Based Boundary Equilibrium Generative Adversarial Networks with Interpretable Representation Learning." Computational Intelligence and Neuroscience 2018 (October 17, 2018): 1–14. http://dx.doi.org/10.1155/2018/6465949.

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This paper describes a new image generation algorithm based on generative adversarial network. With an information-theoretic extension to the autoencoder-based discriminator, this new algorithm is able to learn interpretable representations from the input images. Our model not only adversarially minimizes the Wasserstein distance-based losses of the discriminator and generator but also maximizes the mutual information between small subset of the latent variables and the observation. We also train our model with proportional control theory to keep the equilibrium between the discriminator and t

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Zhang, Senwei. "Research Progress of Image Generation Methods Based on Deep Learning." Highlights in Science, Engineering and Technology 103 (June 26, 2024): 28–35. http://dx.doi.org/10.54097/0999vz62.

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This paper provides an overview of deep learning-based image generation methods and image style migration techniques. The focus is on text-to-image generation, unconditional and conditional image generation methods, and various approaches for image style migration. The review covers the underlying models, including Generative Adversarial Networks (GAN), Variational Autoencoders (VAE), and diffusion models, while also discussing conditional image generation methods such as Conditional Generative Adversarial Networks (CGAN) and Stacked Generative Adversarial Network (StackGAN). Furthermore, it e

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Huang, Zhiwu, Jiqing Wu, and Luc Van Gool. "Manifold-Valued Image Generation with Wasserstein Generative Adversarial Nets." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3886–93. http://dx.doi.org/10.1609/aaai.v33i01.33013886.

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Generative modeling over natural images is one of the most fundamental machine learning problems. However, few modern generative models, including Wasserstein Generative Adversarial Nets (WGANs), are studied on manifold-valued images that are frequently encountered in real-world applications. To fill the gap, this paper first formulates the problem of generating manifold-valued images and exploits three typical instances: hue-saturation-value (HSV) color image generation, chromaticity-brightness (CB) color image generation, and diffusion-tensor (DT) image generation. For the proposed generativ

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Yang, Zhe, Yi Huang, Yaqin Chen, Xiaoting Wu, Junlan Feng, and Chao Deng. "CTGGAN: Controllable Text Generation with Generative Adversarial Network." Applied Sciences 14, no. 7 (2024): 3106. http://dx.doi.org/10.3390/app14073106.

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Controllable Text Generation (CTG) aims to modify the output of a Language Model (LM) to meet specific constraints. For example, in a customer service conversation, responses from the agent should ideally be soothing and address the user’s dissatisfaction or complaints. This imposes significant demands on controlling language model output. However, demerits exist among traditional methods. Promoting and fine-tuning language models exhibit the “hallucination” phenomenon and cannot guarantee complete adherence to constraints. Conditional language models (CLM), which map control codes into LM rep

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Bhandari, Basant Babu, Aakash Raj Dhakal, Laxman Maharjan, and Asmin Karki. "Nepali Handwritten Letter Generation using GAN." Journal of Science and Engineering 9 (December 31, 2021): 49–55. http://dx.doi.org/10.3126/jsce.v9i9.46308.

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The generative adversarial networks seem to work very effectively for training generative deep neural networks. The aim is to generate Nepali Handwritten letters using adversarial training in raster image format. Deep Convolutional generative network is used to generate Nepali handwritten letters. Proposed generative adversarial model that works on Devanagari 36 classes, each having 10,000 images, generates the Nepali Handwritten Letters that are similar to the real-life data-set of total size 360,000 images. The generated letters are obtained by simultaneously training the generator and discr

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Rui Yang, Rui Yang, Tian-Jie Cao Rui Yang, Xiu-Qing Chen Tian-Jie Cao, Feng-rong Zhang Xiu-Qing Chen, and Yun-Yan Qi Feng-rong Zhang. "An Ensemble Denoiser Based on Generative Adversarial Networks to Eliminate Adversarial Perturbations." 電腦學刊 32, no. 5 (2021): 055–75. http://dx.doi.org/10.53106/199115992021103205005.

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Deep neural networks (DNNs) have been applied in various machine learning tasks with the success of deep learning technologies. However, they are surprisingly vulnerable to adversarial examples, which can easily fool deep neural networks. Due to this drawback of deep neural networks, numerous methods have been proposed to eliminate the effect of adversarial examples. Although they do play a significant role in protecting deep neural networks, most of them all have one flaw in common. They are only effective for certain types of adversarial examples. This paper proposes an ensemble denoiser bas

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Yang, Hongyi, Chengqi Xue, Xiaoying Yang, and Han Yang. "Icon Generation Based on Generative Adversarial Networks." Applied Sciences 11, no. 17 (2021): 7890. http://dx.doi.org/10.3390/app11177890.

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Icon design is an important part of UI design, and a design task that designers often encounter. During the design process, it is important to highlight the function of icons themselves and avoid excessive similarity with similar icons, i.e., to have a certain degree of innovation and uniqueness. With the rapid development of deep learning technology, generative adversarial networks (GANs) can be used to assist designers in designing and updating icons. In this paper, we construct an icon dataset consisting of 8 icon categories, and introduce state-of-the-art research and training techniques i

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Gu, Honglei. "Supervised Contrastive Generative Adversarial Networks." Theoretical and Natural Science 5, no. 1 (2023): 234–39. http://dx.doi.org/10.54254/2753-8818/5/20230428.

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Generative Adversarial Networks (GANs) is becoming more and more popular, artists use them to find their own inspirations, computer scientists use it for data synthesis, workers use it for machine fault diagnosis and so on. However, GANs are flawed despite its popularity: they are unstable. GANs are based on game theory. In a typical GAN model, the generator and the discriminator are both improved by competing with each other. Therefore, in this highly competitive training process, GANs can easily run into trouble while they move towards the optimal solution. In most cases, the case of such in

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Yeshwanth Vasa. "ROBUSTNESS AND ADVERSARIAL ATTACKS ON GENERATIVE MODELS." International Journal for Research Publication and Seminar 15, no. 3 (2024): 462–71. http://dx.doi.org/10.36676/jrps.v15.i3.1537.

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Since generative models rely on providing input data samples, it is essential to have a robust generative model capable of standing against adversarial attacks that can tamper with the model's output. This paper employs empirical analysis to examine the weaknesses of critical generative models like GANs and VAEs and additionally discovers the defense schemes. In a controlled environment created by accurately modeled adversarial trial data sets and time-sensitive analyses, we test and compare various confirmed adversarial training methods and defenses, such as implicit generative modeling and p

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Yeshwanth Vasa. "ROBUSTNESS AND ADVERSARIAL ATTACKS ON GENERATIVE MODELS." International Journal for Research Publication and Seminar 12, no. 3 (2021): 462–71. http://dx.doi.org/10.36676/jrps.v12.i3.1537.

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Since generative models rely on providing input data samples, it is essential to have a robust generative model capable of standing against adversarial attacks that can tamper with the model's output. This paper employs empirical analysis to examine the weaknesses of critical generative models like GANs and VAEs and additionally discovers the defense schemes. In a controlled environment created by accurately modeled adversarial trial data sets and time-sensitive analyses, we test and compare various confirmed adversarial training methods and defenses, such as implicit generative modeling and p

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Ren, Zhihang, Stella X. Yu, and David Whitney. "Controllable Medical Image Generation via Generative Adversarial Networks." Electronic Imaging 2021, no. 11 (2021): 112–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.11.hvei-112.

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Radiologists and pathologists frequently make highly consequential perceptual decisions. For example, visually searching for a tumor and recognizing whether it is malignant can have a life-changing impact on a patient. Unfortunately, all human perceivers— even radiologists—have perceptual biases. Because human perceivers (medical doctors) will, for the foreseeable future, be the final judges of whether a tumor is malignant, understanding and mitigating human perceptual biases is important. While there has been research on perceptual biases in medical image perception tasks, the stimuli used fo

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Rajabi, Amirarsalan, and Ozlem Ozmen Garibay. "TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks." Machine Learning and Knowledge Extraction 4, no. 2 (2022): 488–501. http://dx.doi.org/10.3390/make4020022.

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With the increasing reliance on automated decision making, the issue of algorithmic fairness has gained increasing importance. In this paper, we propose a Generative Adversarial Network for tabular data generation. The model includes two phases of training. In the first phase, the model is trained to accurately generate synthetic data similar to the reference dataset. In the second phase we modify the value function to add fairness constraint, and continue training the network to generate data that is both accurate and fair. We test our results in both cases of unconstrained, and constrained f

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Li, Jingtao, Zhanlong Chen, Xiaozhen Zhao, and Lijia Shao. "MapGAN: An Intelligent Generation Model for Network Tile Maps." Sensors 20, no. 11 (2020): 3119. http://dx.doi.org/10.3390/s20113119.

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In recent years, the generative adversarial network (GAN)-based image translation model has achieved great success in image synthesis, image inpainting, image super-resolution, and other tasks. However, the images generated by these models often have problems such as insufficient details and low quality. Especially for the task of map generation, the generated electronic map cannot achieve effects comparable to industrial production in terms of accuracy and aesthetics. This paper proposes a model called Map Generative Adversarial Networks (MapGAN) for generating multitype electronic maps accur

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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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Liu, Lanlan, Yuting Zhang, Jia Deng, and Stefano Soatto. "Dynamically Grown Generative Adversarial Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (2021): 8680–87. http://dx.doi.org/10.1609/aaai.v35i10.17052.

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Recent work introduced progressive network growing as a promising way to ease the training for large GANs, but the model design and architecture-growing strategy still remain under-explored and needs manual design for different image data. In this paper, we propose a method to dynamically grow a GAN during training, optimizing the network architecture and its parameters together with automation. The method embeds architecture search techniques as an interleaving step with gradient-based training to periodically seek the optimal architecture-growing strategy for the generator and discriminator.

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Lee, Minhyeok, and Junhee Seok. "Score-Guided Generative Adversarial Networks." Axioms 11, no. 12 (2022): 701. http://dx.doi.org/10.3390/axioms11120701.

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We propose a generative adversarial network (GAN) that introduces an evaluator module using pretrained networks. The proposed model, called a score-guided GAN (ScoreGAN), is trained using an evaluation metric for GANs, i.e., the Inception score, as a rough guide for the training of the generator. Using another pretrained network instead of the Inception network, ScoreGAN circumvents overfitting of the Inception network such that the generated samples do not correspond to adversarial examples of the Inception network. In addition, evaluation metrics are employed only in an auxiliary role to pre

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Pei-Guang Lin, Pei-Guang Lin, Qing-Tao Li Pei-Guang Lin, Jia-Qian Zhou Qing-Tao Li, Ji-Hou Wang Jia-Qian Zhou, Mu-Wei Jian Ji-Hou Wang, and Chen Zhang Mu-Wei Jian. "Financial Forecasting Method for Generative Adversarial Networks based on Multi-model Fusion." 電腦學刊 34, no. 1 (2023): 131–44. http://dx.doi.org/10.53106/199115992023023401010.

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<p> To some extent, stock prices can reflect national economic development and residents’ living standards. However, current stock forecasts are mainly analyzed using the stock’s own price. It is found that the exchange rate and technical indicators are closely related to the fluctuations of stocks and stock indexes. In this study, a generative adversarial network (GAN) financial forecasting method based on multi-model fusion is proposed, which introduces the exchange rate and technical indexes of stock and stock index into the input data, and combines the characteristics of its own high

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Jian Wu, Jian Wu, Honghui Deng Jian Wu, Fei Cheng Honghui Deng, and Hongjun Wang Fei Cheng. "Camera Tripod Removal Model in Panoramic Images Based on Generative Adversarial Networks." 電腦學刊 34, no. 3 (2023): 019–29. http://dx.doi.org/10.53106/199115992023063403002.

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<p>There are often residual images of the camera tripod in panoramic images, which may reduce the image quality and deteriorate the post-processing speed. To address this problem, a camera tripod removal network (TRNet) based on generative adversarial network&nbsp;is proposed. As an end-to-end model, the generator is designed to include recognition and reconstruction branches, which reduce the number of parameters and improve the training efficiency by sharing the encoder&nbsp;and correspond to scaffold recognition and texture reconstruction respectively. The recognition branch b

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Li, Gang. "Construction of Sports Training Performance Prediction Model Based on a Generative Adversarial Deep Neural Network Algorithm." Computational Intelligence and Neuroscience 2022 (May 21, 2022): 1–11. http://dx.doi.org/10.1155/2022/1211238.

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The generative adversarial neural network algorithm is used for in-depth research and analysis of sports training performance prediction, and the corresponding model is built and used for practical applications. To address the problems of gradient disappearance, training instability, lack of local consistency of repair results, and long training time in the image restoration algorithm based on generative adversarial networks, this paper proposes a multigenerative adversarial image restoration algorithm based on multigranularity reconstruction sampling. The algorithm changes the distribution in

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Li, Haisheng, Yanping Zheng, Xiaoqun Wu, and Qiang Cai. "3D Model Generation and Reconstruction Using Conditional Generative Adversarial Network." International Journal of Computational Intelligence Systems 12, no. 2 (2019): 697. http://dx.doi.org/10.2991/ijcis.d.190617.001.

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Hazra, Debapriya, and Yung-Cheol Byun. "SynSigGAN: Generative Adversarial Networks for Synthetic Biomedical Signal Generation." Biology 9, no. 12 (2020): 441. http://dx.doi.org/10.3390/biology9120441.

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Automating medical diagnosis and training medical students with real-life situations requires the accumulation of large dataset variants covering all aspects of a patient’s condition. For preventing the misuse of patient’s private information, datasets are not always publicly available. There is a need to generate synthetic data that can be trained for the advancement of public healthcare without intruding on patient’s confidentiality. Currently, rules for generating synthetic data are predefined and they require expert intervention, which limits the types and amount of synthetic data. In this

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Yu, Fangchao, Li Wang, Xianjin Fang, and Youwen Zhang. "The Defense of Adversarial Example with Conditional Generative Adversarial Networks." Security and Communication Networks 2020 (August 25, 2020): 1–12. http://dx.doi.org/10.1155/2020/3932584.

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Deep neural network approaches have made remarkable progress in many machine learning tasks. However, the latest research indicates that they are vulnerable to adversarial perturbations. An adversary can easily mislead the network models by adding well-designed perturbations to the input. The cause of the adversarial examples is unclear. Therefore, it is challenging to build a defense mechanism. In this paper, we propose an image-to-image translation model to defend against adversarial examples. The proposed model is based on a conditional generative adversarial network, which consists of a ge

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Bi, Fangming, Zijian Man, Yang Xia, et al. "Improvement and Application of Generative Adversarial Networks Algorithm Based on Transfer Learning." Mathematical Problems in Engineering 2020 (July 13, 2020): 1–11. http://dx.doi.org/10.1155/2020/9453586.

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Generative adversarial networks are currently used to solve various problems and are one of the most popular models. Generator and discriminator are characteristics of continuous game process in training. While improving the quality of generated pictures, it will also make it difficult for the loss function to be stable, and the training speed will be extremely slow compared with other methods. In addition, since the generative adversarial networks directly learns the data distribution of samples, the model will become uncontrollable and the freedom of the model will become too large when the

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S A, Priyanka. "Dynamic Visual Creation: Implementing Text to Image Generation." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48652.

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Abstract—Generative Adversarial Network (GANs) has become one of the most interesting ideas in the last years in Machine Learning. Generative Adversarial Network is a very exciting area and that’s why researchers are so excited about building generative models as they are set to vary what machines can do for humans. This paper proposes the generation of realistic images according to their semantics based on text description using a Knowledge Graph alongside Knowledge Guided Generative Adversarial Network (KG-GAN) that comes with the embeddings generated from the Knowledge Graph (KG) into GAN.

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P. Kamakshi Thai, Sai Jayanth Bandaru, Abhishek Sharma, and Akshay Devala. "Fashion image generation using generative adversarial neural network." World Journal of Advanced Research and Reviews 25, no. 1 (2025): 850–53. https://doi.org/10.30574/wjarr.2025.25.1.0123.

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Fashion image generation is a significant challenge at the intersection of artificial intelligence (AI) and creative industries, with applications in design, e-commerce, and virtual try-on systems. Conditional Generative Adversarial Networks (CGANs) extend the capabilities of standard GANs by allowing control over generated content based on specified conditions, such as clothing type, color, or texture. This Study investigates the use of CGANs for generating high-quality, attribute-specific fashion images. The study includes designing a CGAN architecture, training the model on the Deep Fashion

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Shastry, K. Aditya, B. A. Manjunatha, T. G. Mohan Kumar, and D. U. Karthik. "Generative Adversarial Networks Based Scene Generation on Indian Driving Dataset." Journal of ICT Research and Applications 17, no. 2 (2023): 181–200. http://dx.doi.org/10.5614/itbj.ict.res.appl.2023.17.2.4.

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The rate of advancement in the field of artificial intelligence (AI) has drastically increased over the past twenty years or so. From AI models that can classify every object in an image to realistic chatbots, the signs of progress can be found in all fields. This work focused on tackling a relatively new problem in the current scenario-generative capabilities of AI. While the classification and prediction models have matured and entered the mass market across the globe, generation through AI is still in its initial stages. Generative tasks consist of an AI model learning the features of a giv

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