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Generative Adversarial Networks (GANs) have achieved remarkable achievements in image synthesis.
A generalization of the glivenko-cantelli theorem
Howard G Tucker · 1959
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2017
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Tag disentangled generative adversarial networks for object image re-rendering
Chaoyue Wang, Chaohui Wang, Chang Xu, and Dacheng Tao · 2017
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Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2018
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Not all samples are created equal: Deep learning with importance sampling
Angelos Katharopoulos and François Fleuret · 2018
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An empirical study of example forgetting during deep neural network learning
Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J Gordon · 2018
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Perceptual adversarial networks for image-to-image transformation
Chaoyue Wang, Chang Xu, Chaohui Wang, and Dacheng Tao · 2018
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Transferring gans: generating images from limited data
Yaxing Wang, Chenshen Wu, Luis Herranz, Joost van de Weijer, Abel Gonzalez-Garcia, and Bogdan Raducanu · 2018
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Figr: Few-shot image generation with reptile
Louis Clouâtre and Marc Demers · 2019
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Multistage gan for fabric defect detection
Juhua Liu, Chaoyue Wang, Hai Su, Bo Du, and Dacheng Tao · 2019
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Image generation from small datasets via batch statistics adaptation
Atsuhiro Noguchi and Tatsuya Harada · 2019
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Learn, imagine and create: Text-to-image generation from prior knowledge
Tingting Qiao, Jing Zhang, Duanqing Xu, and Dacheng Tao · 2019
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Mirrorgan: Learning text-to-image generation by redescription
Tingting Qiao, Jing Zhang, Duanqing Xu, and Dacheng Tao · 2019
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Singan: Learning a generative model from a single natural image
Tamar Rott Shaham, Tali Dekel, and Tomer Michaeli · 2019
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A survey on image data augmentation for deep learning
Connor Shorten and Taghi M Khoshgoftaar · 2019
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Resattr-gan: Unpaired deep residual attributes learning for multi-domain face image translation
Rentuo Tao, Ziqiang Li, Renshuai Tao, and Bin Li · 2019
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Evolutionary generative adversarial networks
Chaoyue Wang, Chang Xu, Xin Yao, and Dacheng Tao · 2019
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The face of art: landmark detection and geometric style in portraits
Jordan Yaniv, Yael Newman, and Ariel Shamir · 2019
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An overview of overfitting and its solutions
Xue Ying · 2019
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Ae-ot-gan: Training gans from data specific latent distribution
Dongsheng An, Yang Guo, Min Zhang, Xin Qi, Na Lei, and Xianfang Gu · 2020
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Puppeteergan: Arbitrary portrait animation with semantic-aware appearance transformation
Zhuo Chen, Chaoyue Wang, Bo Yuan, and Dacheng Tao · 2020
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Instance selection for gans
Terrance DeVries, Michal Drozdzal, and Graham W Taylor · 2020
Cited alongside, same era.
Hyungrok Ham, Tae Joon Jun, and Daeyoung Kim · 2020
Cited alongside, same era.
F2gan: Fusing-and-filling gan for few-shot image generation
Yan Hong, Li Niu, Jianfu Zhang, Weijie Zhao, Chen Fu, and Liqing Zhang · 2020
Cited alongside, same era.
Training generative adversarial networks with limited data
Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2020
Cited alongside, same era.
Analyzing and improving the image quality of stylegan
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
Cited alongside, same era.
A review on generative adversarial networks: Algorithms, theory, and applications
Jie Gui, Zhenan Sun, Yonggang Wen, Dacheng Tao, and Jieping Ye · 2021
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Improved techniques for training single-image gans
Tobias Hinz, Matthew Fisher, Oliver Wang, and Stefan Wermter · 2021
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A survey on generative adversarial networks: Variants, applications, and training
Abdul Jabbar, Xi Li, and Bourahla Omar · 2021
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Deceive d: Adaptive pseudo augmentation for gan training with limited data
Liming Jiang, Bo Dai, Wayne Wu, and Chen Change Loy · 2021
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Smoothing the generative latent space with mixup-based distance learning
Chaerin Kong, Jeesoo Kim, Donghoon Han, and Nojun Kwak · 2021
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Yijun Li, Richard Zhang, Jingwan Lu, and Eli Shechtman · 2020
Cited alongside, same era.
A systematic survey of regularization and normalization in gans
Ziqiang Li, Xintian Wu, Muhammad Usman, Rentuo Tao, Pengfei Xia, Huanhuan Chen, and Bin Li · 2020
Cited alongside, same era.
Ziqiang Li, Pengfei Xia, Rentuo Tao, Hongjing Niu, and Bin Li · 2020
Cited alongside, same era.
Towards faster and stabilized gan training for high-fidelity few-shot image synthesis
Bingchen Liu, Yizhe Zhu, Kunpeng Song, and Ahmed Elgammal · 2020
Cited alongside, same era.
Data instance prior (disp) in generative adversarial networks
Puneet Mangla, Nupur Kumari, Mayank Singh, Balaji Krishnamurthy, and Vineeth N Balasubramanian · 2020
Cited alongside, same era.
Freeze the discriminator: a simple baseline for fine-tuning gans
Sangwoo Mo, Minsu Cho, and Jinwoo Shin · 2020
Cited alongside, same era.
Overfitting in adversarially robust deep learning
Leslie Rice, Eric Wong, and Zico Kolter · 2020
Cited alongside, same era.
Nupur Kumari, Richard Zhang, Eli Shechtman, and Jun-Yan Zhu · 2021
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Interpreting the latent space of gans via measuring decoupling
Ziqiang Li, Rentuo Tao, Jie Wang, Fu Li, Hongjing Niu, Mingdao Yue, and Bin Li · 2021
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Are high-frequency components beneficial for training of generative adversarial networks
Ziqiang Li, Pengfei Xia, Xue Rui, Yanghui Hu, and Bin Li · 2021
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Few-shot image generation via cross-domain correspondence
Utkarsh Ojha, Yijun Li, Jingwan Lu, Alexei A Efros, Yong Jae Lee, Eli Shechtman, and Richard Zhang · 2021
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Data augmentation can improve robustness
Sylvestre-Alvise Rebuffi, Sven Gowal, Dan Andrei Calian, Florian Stimberg, Olivia Wiles, and Timothy A Mann · 2021
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Projected gans converge faster
Axel Sauer, Kashyap Chitta, Jens Müller, and Andreas Geiger · 2021
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Generative adversarial networks (gans) challenges, solutions, and future directions
Divya Saxena and Jiannong Cao · 2021
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One-shot gan: Learning to generate samples from single images and videos
Vadim Sushko, Jurgen Gall, and Anna Khoreva · 2021
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On data augmentation for gan training
Ngoc-Trung Tran, Viet-Hung Tran, Ngoc-Bao Nguyen, Trung-Kien Nguyen, and Ngai-Man Cheung · 2021
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Regularizing generative adversarial networks under limited data
Hung-Yu Tseng, Lu Jiang, Ce Liu, Ming-Hsuan Yang, and Weilong Yang · 2021
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Generative adversarial networks in computer vision: A survey and taxonomy
Zhengwei Wang, Qi She, and Tomas E Ward · 2021
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Data-efficient instance generation from instance discrimination
Ceyuan Yang, Yujun Shen, Yinghao Xu, and Bolei Zhou · 2021
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One-shot generative domain adaptation
Ceyuan Yang, Yujun Shen, Zhiyi Zhang, Yinghao Xu, Jiapeng Zhu, Zhirong Wu, and Bolei Zhou · 2021
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Efficient lottery ticket finding: Less data is more
Zhenyu Zhang, Xuxi Chen, Tianlong Chen, and Zhangyang Wang · 2021
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When, why, and which pretrained GANs are useful?
Timofey Grigoryev, Andrey Voynov, and Artem Babenko · 2022
Closest in time.
Exploiting knowledge distillation for few-shot image generation, 2022
Xingzhong Hou, Boxiao Liu, Fang Wan, and Haihang You · 2022
Closest in time.
The role of imagenet classes in fr
Tuomas Kynkäänniemi, Tero Karras, Miika Aittala, Timo Aila, and Jaakko Lehtinen · 2022
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Stylegan-xl: Scaling stylegan to large diverse datasets
Axel Sauer, Katja Schwarz, and Andreas Geiger · 2022
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Collapse by conditioning: Training class-conditional GANs with limited data
Mohamad Shahbazi, Martin Danelljan, Danda Pani Paudel, and Luc Van Gool · 2022
Closest in time.