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Готові списки джерел за темами / GAN Generative Adversarial Network / Статті в журналах

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Статті в журналах з теми "GAN Generative Adversarial Network"

Автор: Grafiati

Опубліковано: 1 червня 2022

Оновлено: 31 липня 2025

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1

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

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Анотація:

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

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2

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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3

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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Анотація:

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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4

Rajbeer, Kaur, Kaur Amanpreet, and Kaur Kirandeep. "Enhancing generative adversarial network." Global Journal of Engineering and Technology Advances 19, no. 1 (2024): 068–73. https://doi.org/10.5281/zenodo.13690760.

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Анотація:

The paper provides a comprehensive review of various GAN methods from the perspectives of theory, and applications. GAN algorithms' mathematical representations, and structures are detailed. The commonalities and differences among these GANs methods are compared. Theoretical issues related to GANs are explored, and typical applications in various fields are showcased. Future scope of research problems for GANs are also discussed in the paper.

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5

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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Анотація:

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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6

Rajbeer Kaur, Amanpreet Kaur, and Kirandeep Kaur. "Enhancing generative adversarial network." Global Journal of Engineering and Technology Advances 19, no. 1 (2024): 068–73. http://dx.doi.org/10.30574/gjeta.2024.19.1.0057.

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Анотація:

The paper provides a comprehensive review of various GAN methods from the perspectives of theory, and applications. GAN algorithms' mathematical representations, and structures are detailed. The commonalities and differences among these GANs methods are compared. Theoretical issues related to GANs are explored, and typical applications in various fields are showcased. Future scope of research problems for GANs are also discussed in the paper.

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7

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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8

Amir, Iqbal, Hamizan Suhaimi, Roslina Mohamad, Ezmin Abdullah, and Chuan-Hsian Pu. "Hybrid encryption based on a generative adversarial network." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 2 (2024): 971. http://dx.doi.org/10.11591/ijeecs.v35.i2.pp971-978.

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In today’s world, encryption is crucial for protecting sensitive data. Neural networks can provide security against adversarial attacks, but meticulous training and vulnerability analysis are required to ensure their effectiveness. Hence, this research explores hybrid encryption based on a generative adversarial network (GAN) for improved message encryption. A neural network was trained using the GAN method to defend against adversarial attacks. Various GAN training parameters were tested to identify the best model system, and various models were evaluated concerning their accuracy against dif

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9

C, Ms Faseela Kathun. "Generating faces From the Sketch Using GAN." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (2021): 1–3. http://dx.doi.org/10.22214/ijraset.2021.38259.

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Abstract: In most cases, sketch images simply show basic profile details and do not include facial detail. As a result, precisely generating facial features is difficult. Using the created adversarial network and attributes, we propose an image translation network. A feature extracting network and a down-sampling up-sampling network make up the generator network. There is a generator and a discriminator in GANs. The Generator creates fake data samples (images, audio, etc.) in intended to mislead the Discriminator. On the other hand, the Discriminator attempts to distinguish between the real an

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10

C, Ms Faseela Kathun. "Generating faces From the Sketch Using GAN." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (2021): 1–3. http://dx.doi.org/10.22214/ijraset.2021.38259.

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Анотація:

Abstract: In most cases, sketch images simply show basic profile details and do not include facial detail. As a result, precisely generating facial features is difficult. Using the created adversarial network and attributes, we propose an image translation network. A feature extracting network and a down-sampling up-sampling network make up the generator network. There is a generator and a discriminator in GANs. The Generator creates fake data samples (images, audio, etc.) in intended to mislead the Discriminator. On the other hand, the Discriminator attempts to distinguish between the real an

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11

Iqbal, Amir Hamizan Suhaimi Roslina Mohamad Ezmin Abdullah Chuan-Hsian Pu. "Hybrid encryption based on a generative adversarial network." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 2 (2024): 971–78. https://doi.org/10.11591/ijeecs.v35.i2.pp971-978.

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Анотація:

In today’s world, encryption is crucial for protecting sensitive data. Neural networks can provide security against adversarial attacks, but meticulous training and vulnerability analysis are required to ensure their effectiveness. Hence, this research explores hybrid encryption based on a generative adversarial network (GAN) for improved message encryption. A neural network was trained using the GAN method to defend against adversarial attacks. Various GAN training parameters were tested to identify the best model system, and various models were evaluated concerning their accuracy again

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12

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

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Анотація:

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

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13

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 (2023): 1–6. http://dx.doi.org/10.36348/merjet.2023.v03i01.001.

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Анотація:

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 highe

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14

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 (2023): 7–11. http://dx.doi.org/10.36348/merjet.2023.v03i01.002.

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Анотація:

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 highe

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15

Mahima, Pandya, and Sonal Rami Prof. "IMAGE GENERATION FROM CAPTION." International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI) 7, no. 1/2 (2018): 01–10. https://doi.org/10.5281/zenodo.6631185.

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Анотація:

Generating images from a text description is as challenging as it is interesting. The Adversarial network performs in a competitive fashion where the networks are the rivalry of each other. With the introduction of Generative Adversarial Network, lots of development is happening in the field of Computer Vision. With generative adversarial networks as the baseline model, studied Stack GAN consisting of two-stage GANS step-by-step in this paper that could be easily understood. This paper presents visual comparative study of other models attempting to generate image conditioned on the text descri

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16

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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17

Lamba, Sahil, Himanshu ., Rohit Kumar Singh, and Er Kamal Soni. "Generative Adversarial Network (GAN) to Generate Realistic Images." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 2190–96. http://dx.doi.org/10.22214/ijraset.2023.50306.

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Abstract: Generative Adversarial Networks (GANs) have rapidly become a focal point of research due to their ability to generate realistic images. First introduced in 2014, GANs have been applied in a multitude of fields such as computer vision and natural language processing, yielding impressive results. Image synthesis is among the most thoroughly researched applications of GANs, and the results thus far have demonstrated the potential of GANs in image synthesis. This paper provides a taxonomy of the methods used in image synthesis, reviews various models for text-to-image synthesis and image

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18

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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Анотація:

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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19

Yang, Ximing, Yuan Wu, Kaiyi Zhang, and Cheng Jin. "CPCGAN: A Controllable 3D Point Cloud Generative Adversarial Network with Semantic Label Generating." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 4 (2021): 3154–62. http://dx.doi.org/10.1609/aaai.v35i4.16425.

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Generative Adversarial Networks (GAN) are good at generating variant samples of complex data distributions. Generating a sample with certain properties is one of the major tasks in the real-world application of GANs. In this paper, we propose a novel generative adversarial network to generate 3D point clouds from random latent codes, named Controllable Point Cloud Generative Adversarial Network(CPCGAN). A two-stage GAN framework is utilized in CPCGAN and a sparse point cloud containing major structural information is extracted as the middle-level information between the two stages. With their

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20

Han, Ligong, Ruijiang Gao, Mun Kim, Xin Tao, Bo Liu, and Dimitris Metaxas. "Robust Conditional GAN from Uncertainty-Aware Pairwise Comparisons." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 10909–16. http://dx.doi.org/10.1609/aaai.v34i07.6723.

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Conditional generative adversarial networks have shown exceptional generation performance over the past few years. However, they require large numbers of annotations. To address this problem, we propose a novel generative adversarial network utilizing weak supervision in the form of pairwise comparisons (PC-GAN) for image attribute editing. In the light of Bayesian uncertainty estimation and noise-tolerant adversarial training, PC-GAN can estimate attribute rating efficiently and demonstrate robust performance in noise resistance. Through extensive experiments, we show both qualitatively and q

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21

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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22

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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23

Gao, Jingjing. "Generative adversarial network based image inpainting." Applied and Computational Engineering 5, no. 1 (2023): 93–98. http://dx.doi.org/10.54254/2755-2721/5/20230540.

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Image inpainting, which is the repair of pixels in damaged areas of an image to make it look as much like the original image as possible. Deep learning-based image inpainting technology is a prominent area of current research interest. This paper focuses on a systematic and comprehensive study of GAN-based image inpainting and presents an analytical summary. Firstly, this paper introduces GAN, which includes the principle of GAN and its mathematical expression. Secondly, the recent GAN-based image inpainting algorithms are summarized, and the advantages and disadvantages of each algorithm are

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24

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 (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 provi

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25

Liu, Aishan, Xianglong Liu, Jiaxin Fan, et al. "Perceptual-Sensitive GAN for Generating Adversarial Patches." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1028–35. http://dx.doi.org/10.1609/aaai.v33i01.33011028.

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Deep neural networks (DNNs) are vulnerable to adversarial examples where inputs with imperceptible perturbations mislead DNNs to incorrect results. Recently, adversarial patch, with noise confined to a small and localized patch, emerged for its easy accessibility in real-world. However, existing attack strategies are still far from generating visually natural patches with strong attacking ability, since they often ignore the perceptual sensitivity of the attacked network to the adversarial patch, including both the correlations with the image context and the visual attention. To address this p

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26

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

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

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27

Tu, Jun, Willies Ogola, Dehong Xu, and Wei Xie. "Intrusion Detection Based on Generative Adversarial Network of Reinforcement Learning Strategy for Wireless Sensor Networks." International Journal of Circuits, Systems and Signal Processing 16 (January 13, 2022): 478–82. http://dx.doi.org/10.46300/9106.2022.16.58.

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Due to the wireless nature of wireless sensor networks (WSN), the network can be deployed in most of the unattended environment, which makes the networks more vulnerable for attackers who may listen to the traffic and inject their own nodes in the sensor network. In our work, we research on a novel machine learning algorithm on intrusion detection based on reinforcement learning (RL) strategy using generative adversarial network (GAN) for WSN which can automatically detect intrusion or malicious attacks into the network. We combine Actor-Critic Algorithm in RL with GAN in a simulated WSN. The

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28

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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29

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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30

Shi, Caijuan, Dongjing Tu, and Jingyi Liu. "Re-GAN: residual generative adversarial network algorithm." Journal of Image and Graphics 26, no. 3 (2021): 594–604. http://dx.doi.org/10.11834/jig.200069.

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31

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

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Анотація:

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 t

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32

Vibhute, Anup, Pranita Bhosale, Nikita Maralbhavi, Shailesh Galande, and Mokshada S. Bhandare. "Face Sketch to Image Generation using Generative Adversarial Network." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10s (2023): 669–75. http://dx.doi.org/10.17762/ijritcc.v11i10s.7706.

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Numerous studies have been conducted in the area of sketch to picture conversion and they got the good outcomes, but sometimes it is not accurate that they observed the blurry boundaries, the mixing of two colors that is the color of hair and face or mixing of both. These results are of the convolution neural networks that are basic of GAN. So to overcome their drawbacks we proposed a novel generative adversarial network using conditional GAN. For that we converted the original image in sketch and both the sketch and original image as reference is applied as input. We got more realistic and sh

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33

Wu, Han. "Face image generation and feature visualization using deep convolutional generative adversarial networks." Journal of Physics: Conference Series 2634, no. 1 (2023): 012041. http://dx.doi.org/10.1088/1742-6596/2634/1/012041.

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Abstract Generative Neural Networks (GAN) aims to generate realistic and recognizable images, including portraits, cartoons and other modalities. Image generation has broad application prospects and important research value in the fields of public security and digital entertainment, and has become one of the current research hotspots. This article will introduce and apply an important image generation model called GAN, which stands for Generative Adversarial Network. Unlike recent image processing models such as Variational Autoencoders (VAE), The discriminative network evaluates potential can

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Dharejo, Fayaz Ali, Farah Deeba, Yuanchun Zhou, et al. "TWIST-GAN: Towards Wavelet Transform and Transferred GAN for Spatio-Temporal Single Image Super Resolution." ACM Transactions on Intelligent Systems and Technology 12, no. 6 (2021): 1–20. http://dx.doi.org/10.1145/3456726.

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Single Image Super-resolution (SISR) produces high-resolution images with fine spatial resolutions from a remotely sensed image with low spatial resolution. Recently, deep learning and generative adversarial networks (GANs) have made breakthroughs for the challenging task of single image super-resolution (SISR) . However, the generated image still suffers from undesirable artifacts such as the absence of texture-feature representation and high-frequency information. We propose a frequency domain-based spatio-temporal remote sensing single image super-resolution technique to reconstruct the HR

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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 (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 fie

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Balint, Adam, and Graham Taylor. "Pal-GAN: Palette-conditioned Generative Adversarial Networks." Journal of Computational Vision and Imaging Systems 6, no. 1 (2021): 1–5. http://dx.doi.org/10.15353/jcvis.v6i1.3536.

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Recent advances in Generative Adversarial Networks (GANs) have shown great progress on a large variety of tasks. A common technique used to yield greater diversity of samples is conditioning on class labels. Conditioning on high-dimensional structured or unstructured information has also been shown to improve generation results, e.g. Image-to-Image translation. The conditioning information is provided in the form of human annotations, which can be expensive and difficult to obtain in cases where domain knowledge experts are needed. In this paper, we present an alternative: conditioning on low-

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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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Fekri, Mohammad Navid, Ananda Mohon Ghosh, and Katarina Grolinger. "Generating Energy Data for Machine Learning with Recurrent Generative Adversarial Networks." Energies 13, no. 1 (2019): 130. http://dx.doi.org/10.3390/en13010130.

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The smart grid employs computing and communication technologies to embed intelligence into the power grid and, consequently, make the grid more efficient. Machine learning (ML) has been applied for tasks that are important for smart grid operation including energy consumption and generation forecasting, anomaly detection, and state estimation. These ML solutions commonly require sufficient historical data; however, this data is often not readily available because of reasons such as data collection costs and concerns regarding security and privacy. This paper introduces a recurrent generative a

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Dong, Chi, Xianhai Pang, Shijie Lu, Jinjian Zhao, Zhaochen Liu, and Jun Xie. "Partial Discharge Data Augmentation and Pattern Recognition for Unbalanced and Small Sample Scenarios." Journal of Physics: Conference Series 2477, no. 1 (2023): 012078. http://dx.doi.org/10.1088/1742-6596/2477/1/012078.

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Abstract To improve the accuracy of partial discharge pattern recognition under unbalanced and small sample conditions, a method of partial discharge data augmentation and pattern recognition using the generative adversarial network embedded deep auto-encoder (DAE-GAN) is proposed. First, deep Auto-encoder (DAE) is embedded into a Generative Adversarial Network (GAN), and DAE is used to guide the generation process to improve the authenticity of generated samples. Second, complement samples of PD samples are added to the training process of the Generative Adversarial Network to solve the probl

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Sumit, Gunjate, Nakhate Tushar, Kshirsagar Tushar, and Sapat Yash. "Sketch to Image using GAN." International Journal of Innovative Science and Research Technology 8, no. 1 (2023): 772–77. https://doi.org/10.5281/zenodo.7588232.

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With the development of the modern age and its technologies, people are discovering ways to improve, streamline, and de-stress their lives. A difficult issue in computer vision and graphics is the creation of realistic visuals from hand-drawn sketches. There are numerous uses for the technique of creating facial sketches from real images and its inverse. Due to the differences between a photo and a sketch, photo/sketch synthesis is still a difficult problem to solve. Existing methods either require precise edge maps or rely on retrieving previously taken pictures. In order to get around the sh

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Zeng, Hongzhi. "Handwriting Digital Image Generation based on GAN: A Comparative Study of Basic GAN and CGAN Models." ITM Web of Conferences 70 (2025): 03019. https://doi.org/10.1051/itmconf/20257003019.

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The vast application of artificial intelligence in numerous fields—image generation being one of them—has been made possible by the quick development of deep learning. Generative Adversarial Networks (GAN) can generate high-quality images through an adversarial training mechanism. The use and performance of GAN and its conditional variation, CGAN, in the field of handwritten digital image generation, are thoroughly examined in this research. The basic GAN and CGAN models, based on the PyTorch deep learning framework and the Modified National Institute of Standards and Technology (MNIST) datase

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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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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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Hemberg, Erik, Jamal Toutouh, Abdullah Al-Dujaili, Tom Schmiedlechner, and Una-May O’reilly. "Spatial Coevolution for Generative Adversarial Network Training." ACM Transactions on Evolutionary Learning and Optimization 1, no. 2 (2021): 1–28. http://dx.doi.org/10.1145/3458845.

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Generative Adversarial Networks (GANs) are difficult to train because of pathologies such as mode and discriminator collapse. Similar pathologies have been studied and addressed in competitive evolutionary computation by increased diversity. We study a system, Lipizzaner, that combines spatial coevolution with gradient-based learning to improve the robustness and scalability of GAN training. We study different features of Lipizzaner’s evolutionary computation methodology. Our ablation experiments determine that communication, selection, parameter optimization, and ensemble optimization each, a

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Kumar, Chandan, Amzad Choudhary, Gurpreet Singh, and Ms Deepti Gupta. "Enhanced Super-Resolution Using GAN." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 2077–80. http://dx.doi.org/10.22214/ijraset.2022.42718.

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Abstract: Super-resolution reconstruction is an increasingly important area in computer vision. To eliminate the problems that super-resolution reconstruction models based on generative adversarial networks are difficult to train and contain artifacts in reconstruction results. besides the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks. However, the hallucinated details are often accompanied with unpleasant artifacts. This paper presented ESRGAN model which was also based on generative adversarial networks. To further

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Kolesnikov, Vladimir Dmitrievich, and Petr Stepanovich Kabalyants. "Application of recurrent neural network models for generating text data of specifications of assembly drawings." Research Result. Information technologies 9, no. 4 (2024): 51–57. https://doi.org/10.18413/2518-1092-2024-9-4-0-6.

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The article explores the capabilities of various models of recurrent neural networks in text data generation. Specifically, classic recurrent network (RNN), long short-term memory network (LSTM) and generative adversarial network (GAN) models are considered in the context of the problem of generating specification text for assembly drawings according to the format approved by government standard. To train the models, a data set in Russian language was used, expanded with additional records simulating input data, consisting of the drawing parts, and expected text of the specifications. The inpu

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Chakraborty, Sujoy. "Camera Fingerprint estimation with a Generative Adversarial Network (GAN)." Electronic Imaging 2021, no. 4 (2021): 336–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.4.mwsf-336.

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For forensic analysis of digital images or videos, the PRNU or camera fingerprint is the most important characteristics, for source attribution and manipulation localization. Typically, a good estimate of the PRNU is obtained by computing its Maximum Likelihood estimate from noise residuals of a large number of flatfield images captured by the camera. In this paper, we propose a novel approach of estimating the fingerprint of a camera, with a Generative Adversarial Network (GAN). The idea is to let the Generator network learn a distribution, from which PRNU samples will be drawn after training

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

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

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Lee, Hyungtak, Seongju Kang, and Kwangsue Chung. "Robust Data Augmentation Generative Adversarial Network for Object Detection." Sensors 23, no. 1 (2022): 157. http://dx.doi.org/10.3390/s23010157.

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Generative adversarial network (GAN)-based data augmentation is used to enhance the performance of object detection models. It comprises two stages: training the GAN generator to learn the distribution of a small target dataset, and sampling data from the trained generator to enhance model performance. In this paper, we propose a pipelined model, called robust data augmentation GAN (RDAGAN), that aims to augment small datasets used for object detection. First, clean images and a small datasets containing images from various domains are input into the RDAGAN, which then generates images that ar

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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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