Journal articles: 'Generative adversarial networks'

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Imamverdiyev, Yadigar, and Firangiz Musayeva. "Analysis of generative adversarial networks." Problems of Information Technology 13, no. 1 (January 24, 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 generative adversarial networks, their types, problems, and advantages, as well as classification and regression, segmentation of medical images, music generation, best description capabilities, text image conversion, video generation, etc. general information is given. In addition, comparisons were made between the generative adversarial network algorithms analyzed on some criteria.

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Thakur, Amey. "Generative Adversarial Networks." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 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 representations. The purpose of this research is to get the reader conversant with the GAN framework as well as to provide the background information on Generative Adversarial Networks, including the structure of both the generator and discriminator, as well as the various GAN variants along with their respective architectures. Applications of GANs are also discussed with examples. Keywords: Generative Adversarial Networks (GANs), Generator, Discriminator, Supervised and Unsupervised Learning, Discriminative and Generative Modelling, Backpropagation, Loss Functions, Machine Learning, Deep Learning, Neural Networks, Convolutional Neural Network (CNN), Deep Convolutional GAN (DCGAN), Conditional GAN (cGAN), Information Maximizing GAN (InfoGAN), Stacked GAN (StackGAN), Pix2Pix, Wasserstein GAN (WGAN), Progressive Growing GAN (ProGAN), BigGAN, StyleGAN, CycleGAN, Super-Resolution GAN (SRGAN), Image Synthesis, Image-to-Image Translation.

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Prozur, Vitalii. "Architecture and Formal-mathematical Justification of Generative Adversarial Networks." Vìsnik Nacìonalʹnogo unìversitetu "Lʹvìvsʹka polìtehnìka". Serìâ Ìnformacìjnì sistemi ta merežì 15 (July 15, 2024): 15–22. http://dx.doi.org/10.23939/sisn2024.15.015.

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The purpose of the work is to analyze the features of generative adversarial networks. The object of research is the process of machine learning algorithmization. The subject of the research is mathematical methods used in the generation of semantically related text. This article explores the architecture and mathematical justification of such a type of generative models as generative adversarial networks. Generative adversarial networks are a powerful tool in the field of artificial intelligence, capable of generating realistic data, including photos, videos, sounds, etc. The architecture of generative competition defines its structure, the interaction of components and a general description of the learning process. Mathematical justification, in turn, includes a theoretical analysis of the principles, algorithms and functions underlying these networks. The article examines the general architecture of generative adversarial networks, examines each of its components (namely, the two main network models – generator and discriminator, their input and output data vectors) and its role in the operation of the algorithm. The author also defined the mathematical principles of generative adversarial networks, focusing on game theory and optimization methods (in particular, special attention is paid to minimax and maximin problems, zero-sum game, saddle points, Nash equilibrium) used in their study. The cost function and the process of deriving it using the Nash equilibrium in a zero-sum game for generative adversarial networks are described, and the learning algorithm using the method of stochastic gradient descent and the mini-batch approach in the form of a pseudocode, its iterations, is visualized network architecture. Finally, the conclusion that generative adversarial networks is an effective tool for creating realistic and believable data samples based on the use of elements of game theory is substantiated. Due to the high quality of generated data, generative adversarial networks can be used in various fields, including: cyber security, medicine, commerce, science, art, etc.

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Chandra, B. Yashas. "Building a Generative Adversarial Network for Image Synthesis." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (July 20, 2024): 1–10. http://dx.doi.org/10.55041/ijsrem36641.

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Generative Adversarial Networks (GANs) have emerged as a powerful class of generative models, capable of synthesizing realistic images by leveraging adversarial training. It explores the process of building a Generative Adversarial Network for image synthesis, delving into the underlying architecture, training methodology, and potential applications. Generative Adversarial Networks typically run unsupervised and use a cooperative zero- sum game framework to learn, where one person's gain equals another person's loss. The proposed Generative Adversarial Network architecture consists of a generator network that learns to create images from random noise and a discriminator network trained to distinguish between real and generated images. Through an adversarial training process, these networks iteratively refine their capabilities, resulting in a generator that produces increasingly realistic pictures and a discriminator with enhanced discriminative abilities. Generative Adversarial Networks are an effective tool for producing realistic, high-quality outputs in a variety of fields, including text and image generation, because of this back-and- forth competition, which results in the creation of increasingly convincing and indistinguishable synthetic data.

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Cai, Zhipeng, Zuobin Xiong, Honghui Xu, Peng Wang, Wei Li, and Yi Pan. "Generative Adversarial Networks." ACM Computing Surveys 54, no. 6 (July 2021): 1–38. http://dx.doi.org/10.1145/3459992.

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Generative Adversarial Networks (GANs) have promoted a variety of applications in computer vision and natural language processing, among others, due to its generative model’s compelling ability to generate realistic examples plausibly drawn from an existing distribution of samples. GAN not only provides impressive performance on data generation-based tasks but also stimulates fertilization for privacy and security oriented research because of its game theoretic optimization strategy. Unfortunately, there are no comprehensive surveys on GAN in privacy and security, which motivates this survey to summarize systematically. The existing works are classified into proper categories based on privacy and security functions, and this survey conducts a comprehensive analysis of their advantages and drawbacks. Considering that GAN in privacy and security is still at a very initial stage and has imposed unique challenges that are yet to be well addressed, this article also sheds light on some potential privacy and security applications with GAN and elaborates on some future research directions.

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Goodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. "Generative adversarial networks." Communications of the ACM 63, no. 11 (October 22, 2020): 139–44. http://dx.doi.org/10.1145/3422622.

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Song, Wenyin, and Haibin Li. "Research on asphalt materials based on machine vision and generate adversarial networks." Highlights in Science, Engineering and Technology 52 (July 4, 2023): 119–24. http://dx.doi.org/10.54097/hset.v52i.8846.

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With the rapid development of artificial intelligence and deep learning, computer vision-oriented generative models have been widely used. Among them, the generation of adversarial network has the most far-reaching influence. To solve the problem of dynamic instability in the training of generative adversarial networks, this paper proposes a rapid construction method of generative adversarial networks based on hidden layer characterization. The generation process of the adversarial network is divided into two independent generation processes to generate the representation of the experience hidden layer and the final result respectively, so as to stably generate the training dynamics of the adversarial network and capture more data patterns. This method can effectively and stably generate the training dynamics of the adversarial network. Finally, theoretical analysis proves that this method can stably generate the training dynamics of adversarial network and reduce the difficulty of adversarial training. Large-scale experiments on multiple data sets demonstrate the effectiveness of the proposed method.

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M. Alghazzawi, Daniyal, Syed Hamid Hasan, and Surbhi Bhatia. "Optimized Generative Adversarial Networks for Adversarial Sample Generation." Computers, Materials & Continua 72, no. 2 (2022): 3877–97. http://dx.doi.org/10.32604/cmc.2022.024613.

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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 (March 25, 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 conduct a comparative analysis of various types of generative networks which will serve as a reference point for the development of the proposed Generative Adversarial Network. The application part of the paper focuses on the practical implementation and utilization of the developed Generative Adversarial Network for the generation of multi-skin tone portraits. By constructing a face dataset specifically designed to incorporate information about ethnicity and skin color, this approach can overcome a limitation associated with traditional generation networks, which typically generate only a single skin color.

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Huang, Yueming, and Jianhua He. "Advancing Architectural Design Through Generative Adversarial Network Deep Learning Technology." International Journal of Distributed Systems and Technologies 15, no. 1 (August 29, 2024): 1–15. http://dx.doi.org/10.4018/ijdst.353305.

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Recent advancements in deep learning have popularized Generative Adversarial Networks for image generation. This study investigates integrating Generative Adversarial Networks technology into architectural design to empower architects in creating diverse, innovative, and practical designs. By analyzing architectural research, deep learning theory, and practical Generative Adversarial Networks applications, we substantiate the feasibility of using Generative Adversarial Networks for architectural design optimization. The generated architectural images exhibit significant diversity, innovation, and practicality, inspiring architects with numerous design possibilities. Overall, Generative Adversarial Networks technology not only expands design methodologies but also stimulates groundbreaking innovation in architectural practice. As technology progresses, Generative Adversarial Networks-based architectural design optimization shows promising potential for widespread adoption, heralding a new era of creativity and efficiency in architecture.

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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 (October 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 based on generative adversarial networks (GANs) to protect deep neural networks. This proposed method aims to remove the effect of multiple types of adversarial examples before they are fed into deep neural networks. Therefore, it is model-independent and cannot modify deep neural networks’ parameters. We employ a generative adversarial network for this proposed method to learn multiple mappings between adversarial examples and benign examples. Each mapping behaves differently for different types of adversarial examples. Therefore, we integrate these mappings as the ultimate method to defend against multiple types of adversarial examples. Experiments are conducted on the MNIST and CIFAR10 datasets. We compare this proposed method with several existing excellent methods. Results show that this proposed method achieves better performance than other methods when defending against multiple types of adversarial examples. The code is available at <a href="https://github.com/Afreadyang/ensemble-ape-gan">https://github.com/Afreadyang/ensemble-ape-gan</a>

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Huang, Wenqi, Lingyu Liang, Zhen Dai, Shang Cao, Huanming Zhang, Xiangyu Zhao, Jiaxuan Hou, Hanju Li, Wenhao Ma, and Liang Che. "Scenario Reduction of Power Systems with Renewable Generations Using Improved Time-GAN." Journal of Physics: Conference Series 2662, no. 1 (December 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 scenario generation, the silhouette coefficient method is used to improve K-means for constructing a scenario reduction model. Finally, the case analysis shows that the proposed method can obtain typical renewable energy power scenarios with spatiotemporal correlation and provide a reference for the analysis of power system operation scenarios.

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Hou, Liang, Zehuan Yuan, Lei Huang, Huawei Shen, Xueqi Cheng, and Changhu Wang. "Slimmable Generative Adversarial Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (May 18, 2021): 7746–53. http://dx.doi.org/10.1609/aaai.v35i9.16946.

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Generative adversarial networks (GANs) have achieved remarkable progress in recent years, but the continuously growing scale of models make them challenging to deploy widely in practical applications. In particular, for real-time generation tasks, different devices require generators of different sizes due to varying computing power. In this paper, we introduce slimmable GANs (SlimGANs), which can flexibly switch the width of the generator to accommodate various quality-efficiency trade-offs at runtime. Specifically, we leverage multiple discriminators that share partial parameters to train the slimmable generator. To facilitate the consistency between generators of different widths, we present a stepwise inplace distillation technique that encourages narrow generators to learn from wide ones. As for class-conditional generation, we propose a sliceable conditional batch normalization that incorporates the label information into different widths. Our methods are validated, both quantitatively and qualitatively, by extensive experiments and a detailed ablation study.

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Choi, Seok-Hwan, Jin-Myeong Shin, Peng Liu, and Yoon-Ho Choi. "ARGAN: Adversarially Robust Generative Adversarial Networks for Deep Neural Networks Against Adversarial Examples." IEEE Access 10 (2022): 33602–15. http://dx.doi.org/10.1109/access.2022.3160283.

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Saxena, Divya, and Jiannong Cao. "Generative Adversarial Networks (GANs)." ACM Computing Surveys 54, no. 3 (June 2021): 1–42. http://dx.doi.org/10.1145/3446374.

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Generative Adversarial Networks (GANs) is a novel class of deep generative models that has recently gained significant attention. GANs learn complex and high-dimensional distributions implicitly over images, audio, and data. However, there exist major challenges in training of GANs, i.e., mode collapse, non-convergence, and instability, due to inappropriate design of network architectre, use of objective function, and selection of optimization algorithm. Recently, to address these challenges, several solutions for better design and optimization of GANs have been investigated based on techniques of re-engineered network architectures, new objective functions, and alternative optimization algorithms. To the best of our knowledge, there is no existing survey that has particularly focused on the broad and systematic developments of these solutions. In this study, we perform a comprehensive survey of the advancements in GANs design and optimization solutions proposed to handle GANs challenges. We first identify key research issues within each design and optimization technique and then propose a new taxonomy to structure solutions by key research issues. In accordance with the taxonomy, we provide a detailed discussion on different GANs variants proposed within each solution and their relationships. Finally, based on the insights gained, we present promising research directions in this rapidly growing field.

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Chao, Xiaopeng, Jiangzhong Cao, Yuqin Lu, Qingyun Dai, and Shangsong Liang. "Constrained Generative Adversarial Networks." IEEE Access 9 (2021): 19208–18. http://dx.doi.org/10.1109/access.2021.3054822.

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Li, Chao, Kelu Yao, Jin Wang, Boyu Diao, Yongjun Xu, and Quanshi Zhang. "Interpretable Generative Adversarial Networks." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (June 28, 2022): 1280–88. http://dx.doi.org/10.1609/aaai.v36i2.20015.

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Learning a disentangled representation is still a challenge in the field of the interpretability of generative adversarial networks (GANs). This paper proposes a generic method to modify a traditional GAN into an interpretable GAN, which ensures that filters in an intermediate layer of the generator encode disentangled localized visual concepts. Each filter in the layer is supposed to consistently generate image regions corresponding to the same visual concept when generating different images. The interpretable GAN learns to automatically discover meaningful visual concepts without any annotations of visual concepts. The interpretable GAN enables people to modify a specific visual concept on generated images by manipulating feature maps of the corresponding filters in the layer. Our method can be broadly applied to different types of GANs. Experiments have demonstrated the effectiveness of our method.

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Wang, Chaoyue, Chang Xu, Xin Yao, and Dacheng Tao. "Evolutionary Generative Adversarial Networks." IEEE Transactions on Evolutionary Computation 23, no. 6 (December 2019): 921–34. http://dx.doi.org/10.1109/tevc.2019.2895748.

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Zhu, Banghua, Jiantao Jiao, and David Tse. "Deconstructing Generative Adversarial Networks." IEEE Transactions on Information Theory 66, no. 11 (November 2020): 7155–79. http://dx.doi.org/10.1109/tit.2020.2983698.

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Mitrofanov, E., and V. Grishkin. "Generative Adversarial Networks Quantization." Physics of Particles and Nuclei 55, no. 3 (June 2024): 563–65. http://dx.doi.org/10.1134/s1063779624030596.

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Zhang, Pengfei, and Xiaoming Ju. "Adversarial Sample Detection with Gaussian Mixture Conditional Generative Adversarial Networks." Mathematical Problems in Engineering 2021 (September 13, 2021): 1–18. http://dx.doi.org/10.1155/2021/8268249.

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It is important to detect adversarial samples in the physical world that are far away from the training data distribution. Some adversarial samples can make a machine learning model generate a highly overconfident distribution in the testing stage. Thus, we proposed a mechanism for detecting adversarial samples based on semisupervised generative adversarial networks (GANs) with an encoder-decoder structure; this mechanism can be applied to any pretrained neural network without changing the network’s structure. The semisupervised GANs also give us insight into the behavior of adversarial samples and their flow through the layers of a deep neural network. In the supervised scenario, the latent feature of the semisupervised GAN and the target network’s logit information are used as the input of the external classifier support vector machine to detect the adversarial samples. In the unsupervised scenario, first, we proposed a one-class classier based on the semisupervised Gaussian mixture conditional generative adversarial network (GM-CGAN) to fit the joint feature information of the normal data, and then, we used a discriminator network to detect normal data and adversarial samples. In both supervised scenarios and unsupervised scenarios, experimental results show that our method outperforms latest methods.

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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 discriminator of the network. Constructed discriminator networks and generator networks both have five convolution layers and the activation function is chosen such that generator networks generate the image and discriminator networks check if the generated image is similar to a real-life image dataset. To measure the quantitative performance, Frechet Inception Distance (FID) methodology is used. The FID value of 18 random samples, generated by networks constructed, is 38413677.145. For a qualitative measure of the model let the reader judge the quality of the image generated by the generator trained model. The Nepali letters were generated by the adversarial network as required. The evaluation helps the generative model to be better and further enables a better generation that humans have not thought of.

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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 explores deep learning-based image style migration methods, including both image iteration-based and model iteration-based approaches. The possibilities and opportunities for the advancement and use of these techniques in the field of picture production are highlighted in the review's conclusion.

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Lee, Minhyeok, and Junhee Seok. "Score-Guided Generative Adversarial Networks." Axioms 11, no. 12 (December 7, 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 prevent overfitting. When evaluated using the CIFAR-10 dataset, ScoreGAN achieved an Inception score of 10.36 ± 0.15, which corresponds to state-of-the-art performance. To generalize the effectiveness of ScoreGAN, the model was evaluated further using another dataset, CIFAR-100. ScoreGAN outperformed other existing methods, achieving a Fréchet Inception distance (FID) of 13.98.

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Wang, Jinrui, Baokun Han, Huaiqian Bao, Mingyan Wang, Zhenyun Chu, and Yuwei Shen. "Data augment method for machine fault diagnosis using conditional generative adversarial networks." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 234, no. 12 (June 7, 2020): 2719–27. http://dx.doi.org/10.1177/0954407020923258.

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As a useful data augmentation technique, generative adversarial networks have been successfully applied in fault diagnosis field. But traditional generative adversarial networks can only generate one category fault signals in one time, which is time-consuming and costly. To overcome this weakness, we develop a novel fault diagnosis method which combines conditional generative adversarial networks and stacked autoencoders, and both of them are built by stacking one-dimensional full connection layers. First, conditional generative adversarial networks is used to generate artificial samples based on the frequency samples, and category labels are adopted as the conditional information to simultaneously generate different category signals. Meanwhile, spectrum normalization is added to the discriminator of conditional generative adversarial networks to enhance the model training. Then, the augmented training samples are transferred to stacked autoencoders for feature extraction and fault classification. Finally, two datasets of bearing and gearbox are employed to investigate the effectiveness of the proposed conditional generative adversarial network–stacked autoencoder method.

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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 (April 30, 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 success in generating high-quality images from input data. The focus of this paper is to propose a new technique for generating highquality images from text descriptions using Stack Generative Adversarial Networks (StackGAN). Through a sketch-refinement process, the problem is also divided into smaller manageable problems. The proposed StackGAN model comprises two stages, Stage-I and Stage-II. Stage-I GAN generates low-resolution images by sketching the primitive shape and colors of the object based on the provided textual description. Stage-II GAN generates high-resolution photo-realistic images with refined details by taking the Stage-I results and textual descriptions as inputs, along with detecting defects and adding details.

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Fu Jie Tey, Fu Jie Tey, Tin-Yu Wu Fu Jie Tey, Yueh Wu Tin-Yu Wu, and Jiann-Liang Chen Yueh Wu. "Generative Adversarial Network for Simulation of Load Balancing Optimization in Mobile Networks." 網際網路技術學刊 23, no. 2 (March 2022): 297–304. http://dx.doi.org/10.53106/160792642022032302010.

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<p>The commercial operation of 5G networks is almost ready to be launched, but problems related to wireless environment, load balancing for example, remain. Many load balancing methods have been proposed, but they were implemented in simulation environments that greatly differ from 5G networks. Current load balancing algorithms, on the other hand, focus on the selection of appropriate Wi-Fi or macro & small cells for Device to Device (D2D) communications, but Wi-Fi facilities and small cells are not available all the time. For this reason, we propose to use the macro cells that provide large coverage to achieve load balancing. By combing Generative Adversarial Network (GAN) with the ns-3 network simulator, this paper uses neural networks in TensorFlow to optimize load balancing of mobile networks, increase the data throughput and reduce the packet loss rate. In addition, to discuss the load balancing problem, we take the data produced by the ns-3 network simulator as the real data for GAN.</p> <p> </p>

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Iranmanesh, Seyed Mehdi, and Nasser M. Nasrabadi. "HGAN: Hybrid generative adversarial network." Journal of Intelligent & Fuzzy Systems 40, no. 5 (April 22, 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 likelihood framework in a joint training manner which diversify the estimated density in order to cover different modes. We propose to use an adversarial network to transfer knowledge from an autoregressive model (teacher) to the generator (student) of a GAN model. A novel deep architecture within the GAN formulation is developed to adversarially distill the autoregressive model information in addition to simple GAN training approach. We conduct extensive experiments on real-world datasets (i.e., MNIST, CIFAR-10, STL-10) to demonstrate the effectiveness of the proposed HGAN under qualitative and quantitative evaluations. The experimental results show the superiority and competitiveness of our method compared to the baselines.

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Sajeeda, Afia, and B. M. Mainul Hossain. "Exploring generative adversarial networks and adversarial training." International Journal of Cognitive Computing in Engineering 3 (June 2022): 78–89. http://dx.doi.org/10.1016/j.ijcce.2022.03.002.

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Ring, Markus, Daniel Schlör, Dieter Landes, and Andreas Hotho. "Flow-based network traffic generation using Generative Adversarial Networks." Computers & Security 82 (May 2019): 156–72. http://dx.doi.org/10.1016/j.cose.2018.12.012.

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Wang, Xuan, Lijun Sun, Abdellah Chehri, and Yongchao Song. "A Review of GAN-Based Super-Resolution Reconstruction for Optical Remote Sensing Images." Remote Sensing 15, no. 20 (October 21, 2023): 5062. http://dx.doi.org/10.3390/rs15205062.

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High-resolution images have a wide range of applications in image compression, remote sensing, medical imaging, public safety, and other fields. The primary objective of super-resolution reconstruction of images is to reconstruct a given low-resolution image into a corresponding high-resolution image by a specific algorithm. With the emergence and swift advancement of generative adversarial networks (GANs), image super-resolution reconstruction is experiencing a new era of progress. Unfortunately, there has been a lack of comprehensive efforts to bring together the advancements made in the field of super-resolution reconstruction using generative adversarial networks. Hence, this paper presents a comprehensive overview of the super-resolution image reconstruction technique that utilizes generative adversarial networks. Initially, we examine the operational principles of generative adversarial networks, followed by an overview of the relevant research and background information on reconstructing remote sensing images through super-resolution techniques. Next, we discuss significant research on generative adversarial networks in high-resolution image reconstruction. We cover various aspects, such as datasets, evaluation criteria, and conventional models used for image reconstruction. Subsequently, the super-resolution reconstruction models based on generative adversarial networks are categorized based on whether the kernel blurring function is recognized and utilized during training. We provide a brief overview of the utilization of generative adversarial network models in analyzing remote sensing imagery. In conclusion, we present a prospective analysis of forthcoming research directions pertaining to super-resolution reconstruction methods that rely on generative adversarial networks.

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Sun, Guangling, Yuying Su, Chuan Qin, Wenbo Xu, Xiaofeng Lu, and Andrzej Ceglowski. "Complete Defense Framework to Protect Deep Neural Networks against Adversarial Examples." Mathematical Problems in Engineering 2020 (May 11, 2020): 1–17. http://dx.doi.org/10.1155/2020/8319249.

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Although Deep Neural Networks (DNNs) have achieved great success on various applications, investigations have increasingly shown DNNs to be highly vulnerable when adversarial examples are used as input. Here, we present a comprehensive defense framework to protect DNNs against adversarial examples. First, we present statistical and minor alteration detectors to filter out adversarial examples contaminated by noticeable and unnoticeable perturbations, respectively. Then, we ensemble the detectors, a deep Residual Generative Network (ResGN), and an adversarially trained targeted network, to construct a complete defense framework. In this framework, the ResGN is our previously proposed network which is used to remove adversarial perturbations, and the adversarially trained targeted network is a network that is learned through adversarial training. Specifically, once the detectors determine an input example to be adversarial, it is cleaned by ResGN and then classified by the adversarially trained targeted network; otherwise, it is directly classified by this network. We empirically evaluate the proposed complete defense on ImageNet dataset. The results confirm the robustness against current representative attacking methods including fast gradient sign method, randomized fast gradient sign method, basic iterative method, universal adversarial perturbations, DeepFool method, and Carlini & Wagner method.

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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 (March 24, 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 (GAN) models to construct intricate images, such as Least Squares Generative Adversarial Networks (LSGAN), Deep Convolutional Generative Adversarial Networks (DCGAN), and Wasserstein Generative Adversarial Networks (WGAN). This paper also discusses, in particular, the quality of the images produced by various GANs with different parameters. For processing, we use a picture with a specific number and scale. The Structural Similarity Index (SSIM) and Mean Squared Error (MSE) will be used to measure image consistency. Between the generated image and the corresponding real image, the SSIM values will be compared. As a result, the images display a strong similarity to the real image when using more training images. LSGAN outperformed other GAN models in the experiment with maximum SSIM values achieved using 200 images as inputs, 2000 epochs, and size 32 × 32.

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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 (October 23, 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 address these challenges, we introduce a blurring preprocessing step on a portion of the training dataset, which is then fed to the discriminator as fake data to encourage the model to avoid blurry edges. Furthermore, we incorporate regulation into the optimizer to mitigate mode collapse. Additionally, memory data stored in the memory repository is presented to the model every epoch to alleviate catastrophic forgetting, thereby enhancing performance throughout the training process. Experimental results demonstrate that BaMSGAN outperforms prior work in anime face generation, significantly reducing distortion rates and accelerating shape convergence.

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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 (August 26, 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 including attention mechanism and spectral normalization based on the original StyleGAN. The results show that our model can effectively generate high-quality icons. In addition, based on the user study, we demonstrate that our generated icons can be useful to designers as design aids. Finally, we discuss the potential impacts and consider the prospects for future related research.

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Asyaev, G. D. "Prospects for Generative - Adversarial Networks in Network Traffic Classification Tasks." Journal of Physics: Conference Series 2096, no. 1 (November 1, 2021): 012174. http://dx.doi.org/10.1088/1742-6596/2096/1/012174.

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Abstract The paper presents an approach that allows increasing the training sample and reducing class imbalance for traffic classification problems. The basic principles and architecture of generative adversarial networks are considered. The mathematical model of network traffic classification is described. The training sample taken to solve the problem has been analyzed. The data proprocessing is carried out and justified. An architecture of the generative-adversarial network is constructed and an algorithm for generating new features is developed. Machine learning models for traffic classification problem were considered and built: Logistic regression, k Nearest Neighbors, Decision tree, Random forest. A comparative analysis of the results of machine learning models without and with the generation of new features is conducted. The obtained results can be applied both in the tasks of network traffic classification, and in general cases of multiclass classification and exclusion of unbalanced features.

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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 (January 20, 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 summarized.

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Di Giammarco, Marcello, Antonella Santone, Mario Cesarelli, Fabio Martinelli, and Francesco Mercaldo. "Evaluating Deep Learning Resilience in Retinal Fundus Classification with Generative Adversarial Networks Generated Images." Electronics 13, no. 13 (July 4, 2024): 2631. http://dx.doi.org/10.3390/electronics13132631.

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The evaluation of Generative Adversarial Networks in the medical domain has shown significant potential for various applications, including adversarial machine learning on medical imaging. This study specifically focuses on assessing the resilience of Convolutional Neural Networks in differentiating between real and Generative Adversarial Network-generated retinal images. The main contributions of this research include the training and testing of Convolutional Neural Networks to evaluate their ability to distinguish real images from synthetic ones. By identifying networks with optimal performances, the study ensures the development of better models for diagnostic classification, enhancing generalization and resilience to adversarial images. Overall, the aim of the study is to demonstrate that the application of Generative Adversarial Networks can improve the resilience of the tested networks, resulting in better classifiers for retinal images. In particular, a network developed by authors, i.e., Standard_CNN, reports the best performance with accuracy equal to 1.

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Berezsky, O. M., and P. B. Liashchynskyi. "METHOD OF GENERATIVE-ADVERSARIAL NETWORKS SEARCHING ARCHITECTURES FOR BIOMEDICAL IMAGES SYNTHESIS." Radio Electronics, Computer Science, Control, no. 1 (April 2, 2024): 104. http://dx.doi.org/10.15588/1607-3274-2024-1-10.

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Context. The article examines the problem of automatic design of architectures of generative-adversarial networks. Generativeadversarial networks are used for image synthesis. This is especially true for the synthesis of biomedical images – cytological and histological, which are used to make a diagnosis in oncology. The synthesized images are used to train convolutional neural networks. Convolutional neural networks are currently among the most accurate classifiers of biomedical images. Objective. The aim of the work is to develop an automatic method for searching for architectures of generative-adversarial networks based on a genetic algorithm. Method. The developed method consists of the stage of searching for the architecture of the generator with a fixed discriminator and the stage of searching for the architecture of the discriminator with the best generator. At the first stage, a fixed discriminator architecture is defined and a generator is searched for. Accordingly, after the first step, the architecture of the best generator is obtained, i.e. the model with the lowest FID value. At the second stage, the best generator architecture was used and a search for the discriminator architecture was carried out. At each cycle of the optimization algorithm, a population of discriminators is created. After the second step, the architecture of the generative-adversarial network is obtained. Results. Cytological images of breast cancer on the Zenodo platform were used to conduct the experiments. As a result of the study, an automatic method for searching for architectures of generatively adversarial networks has been developed. On the basis of computer experiments, the architecture of a generative adversarial network for the synthesis of cytological images was obtained. The total time of the experiment was ~39.5 GPU hours. As a result, 16,000 images were synthesized (4000 for each class). To assess the quality of synthesized images, the FID metric was used.The results of the experiments showed that the developed architecture is the best. The network’s FID value is 3.39. This result is the best compared to well-known generative adversarial networks. Conclusions. The article develops a method for searching for architectures of generative-adversarial networks for the problems of synthesis of biomedical images. In addition, a software module for the synthesis of biomedical images has been developed, which can be used to train CNN.

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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 in the network structure is replaced with a deep-space separable convolution to improve the real-time performance of the model by reducing the model parameters. Through extensive testing on real datasets, the results show that the model in this paper can generate higher performance ethnic culture symbols while maintaining better temporal performance, which has some practical application value.

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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 (April 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 Networks (cWGAN) is combined with Convolutional Neural Networks (CNN) to create the generative modeling architecture [1]. The generative model is implemented with a conditional proponent to allow the TSCS targeted design generation and is enhanced with the coordinate convolution (CordConv) layer to improve the model’s spatial recognition of cylinder configurations. The cWGAN model [1] is capable of generating images of 2D configurations of scatterers that exhibit low scattering. The method is demonstrated by giving examples of generating 2-cylinder and 4-cylinder planar configurations with minimal TSCS. [1]. P. Lai, F. Amirkulova, and P. Gerstoft. “Conditional Wasserstein generative adversarial networks applied to acoustic metamaterial design,” J. Acoust. Soc. Am. 150(6), 4362–4374 (2021).

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Reddi, Surya Prakasa Rao, Madhusudhana Rao T.V., Srinivasa Rao P., and Prakash Bethapudi. "An Efficient Method for Facial Sketches Synthesization Using Generative Adversarial Networks." Webology 19, no. 1 (January 20, 2022): 3119–29. http://dx.doi.org/10.14704/web/v19i1/web19206.

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The synthesis of facial sketches is an important technique in digital entertainment and law enforcement agencies. Recent advancements in deep learning have shown its possibility in generating images/sketches using attribute guided features. Facial features are important attributes because they determine human faces' detailed description and appearance during sketch generation. Traditionally, the forensic or composite artist has to sketch by interviewing witnesses manually. To automate this process of face sketch generation, a deep learning-based generative adversarial network incorporated with multiple activation functions is proposed for its efficiency improvement. The proposed model is extensively tested using different evaluation metrics such as RMSE, PSNR, SSIM, SRE, SAM, UIQ & BRISQUE.

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Agarwal, Arun, Swatishee Chhotaray, Niraj Kumar Roul, and Saurabh N. Mehta. "A Review Super Resolution Using Generative Adversarial Network-Applications and Challenges." Middle East Research Journal of Engineering and Technology 3, no. 1 (January 12, 2023): 1–6. http://dx.doi.org/10.36348/merjet.2023.v03i01.001.

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Artificial Neural Networks (ANNs), Deep Learning, and AI Deep learning is a subset of machine learning. In order to create pictures with a greater resolution, a high-resolution GAN combines a deep network with an opponent network. An approach to generative modelling that uses deep learning techniques like convolutional neural networks is known as generative adversarial networks, or GAN. In the Super-Resolution Generative Adversarial Network (SRGAN), a Generative Adversarial Network (GAN) may transform low-resolution images into super resolution images that are more finely detailed and of higher quality. In the past, CNNs were used to produce incredibly precise and detailed images. However, they could have trouble recalling specifics and usually draw hazy visuals which can be overcome by SRGAN.

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Priyanka, Kumari, Sneha Shrishti, and Siddhi Jain. "4G/VoLTE- Spectrum Sensing using Cyclostationary Detection (CS-SS) Method: A Review." Middle East Research Journal of Engineering and Technology 3, no. 1 (January 14, 2023): 7–11. http://dx.doi.org/10.36348/merjet.2023.v03i01.002.

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Artificial Neural Networks (ANNs), Deep Learning, and AI Deep learning is a subset of machine learning. In order to create pictures with a greater resolution, a high-resolution GAN combines a deep network with an opponent network. An approach to generative modelling that uses deep learning techniques like convolutional neural networks is known as generative adversarial networks, or GAN. In the Super-Resolution Generative Adversarial Network (SRGAN), a Generative Adversarial Network (GAN) may transform low-resolution images into super resolution images that are more finely detailed and of higher quality. In the past, CNNs were used to produce incredibly precise and detailed images. However, they could have trouble recalling specifics and usually draw hazy visuals which can be overcome by SRGAN.

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Songyuan, Li, Mengxuan Fan, and Renqiu Chen. "Overview of Generative Adversarial Networks." Journal of Physics: Conference Series 1873, no. 1 (April 1, 2021): 012071. http://dx.doi.org/10.1088/1742-6596/1873/1/012071.

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Qian, Wenliang, Yang Xu, Wangmeng Zuo, and Hui Li. "Self-Sparse Generative Adversarial Networks." CAAI Artificial Intelligence Research 1, no. 1 (September 2022): 68–78. http://dx.doi.org/10.26599/air.2022.9150005.

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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 (May 18, 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. It enjoys the benefits of both eased training because of progressive growing and improved performance because of broader architecture design space. Experimental results demonstrate new state-of-the-art of image generation. Observations in the search procedure also provide constructive insights into the GAN model design such as generator-discriminator balance and convolutional layer choices.

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Skandarani, Youssef, Alain Lalande, Jonathan Afilalo, and Pierre-Marc Jodoin. "Generative Adversarial Networks in Cardiology." Canadian Journal of Cardiology 38, no. 2 (February 2022): 196–203. http://dx.doi.org/10.1016/j.cjca.2021.11.003.

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Chen, Yao, Qingyi Gao, and Xiao Wang. "Inferential Wasserstein generative adversarial networks." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 84, no. 1 (November 15, 2021): 83–113. http://dx.doi.org/10.1111/rssb.12476.

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Zhai, Zhonghua. "Auto-encoder generative adversarial networks." Journal of Intelligent & Fuzzy Systems 35, no. 3 (October 1, 2018): 3043–49. http://dx.doi.org/10.3233/jifs-169659.

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Journal articles on the topic 'Generative adversarial networks'

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