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Training generative adversarial networks (GANs) with limited real image data generally results in deteriorated performance and collapsed models.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky et al · 2009
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Learning hybrid image templates (hit) by information projection
Zhangzhang Si and Song-Chun Zhu · 2011
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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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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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
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Deep generative image models using a laplacian pyramid of adversarial networks
Emily L Denton, Soumith Chintala, Rob Fergus, et al · 2015
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Adversarial machine learning at scale
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
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Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
Song Han, Huizi Mao, and William J. Dally · 2016
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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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Learning from simulated and unsupervised images through adversarial training
Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Josh Susskind, Wenda Wang, and Russ Webb · 2017
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Towards principled methods for training generative adversarial networks
Martin Arjovsky and Léon Bottou · 2017
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Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks
Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, and Dimitris N Metaxas · 2017
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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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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Mmd gan: Towards deeper understanding of moment matching network
Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, and Barnabas Poczos · 2017
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Data augmentation generative adversarial networks
Antreas Antoniou, Amos Storkey, and Harrison Edwards · 2017
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Few-shot learning with graph neural networks
Victor Garcia and Joan Bruna · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Tiny imagenet challenge
Jiayu Wu, Qixiang Zhang, and Guoxi Xu · 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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Learning efficient convolutional networks through network slimming
Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan, and Changshui Zhang · 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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Spectral Normalization for Generative Adversarial Networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
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cgans with projection discriminator
Takeru Miyato and Masanori Koyama · 2018
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Pa-gan: Improving gan training by progressive augmentation
Dan Zhang and Anna Khoreva · 2018
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Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
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Which training methods for gans do actually converge?
Lars Mescheder, Andreas Geiger, and Sebastian Nowozin · 2018
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Adversarially robust generalization requires more data
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, and Aleksander Madry · 2018
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Robustness may be at odds with accuracy
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry · 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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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Deblurgan-v2: Deblurring (orders-of-magnitude) faster and better
Orest Kupyn, Tetiana Martyniuk, Junru Wu, and Zhangyang Wang · 2019
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Autogan: Neural architecture search for generative adversarial networks
Xinyu Gong, Shiyu Chang, Yifan Jiang, and Zhangyang Wang · 2019
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
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Consistency regularization for generative adversarial networks
Han Zhang, Zizhao Zhang, Augustus Odena, and Honglak Lee · 2019
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Self-attention generative adversarial networks
Small-gan: Speeding up gan training using core-sets
Samarth Sinha, Han Zhang, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, and Augustus Odena · 2020
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Terrance DeVries, Michal Drozdzal, and Graham W Taylor · 2020
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Linear mode connectivity and the lottery ticket hypothesis
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, and Michael Carbin · 2020
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Comparing rewinding and fine-tuning in neural network pruning
Alex Renda, Jonathan Frankle, and Michael Carbin · 2020
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Picking winning tickets before training by preserving gradient flow
Chaoqi Wang, Guodong Zhang, and Roger Grosse · 2020
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The early phase of neural network training
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Han Zhang, Ian Goodfellow, Dimitris Metaxas, and Augustus Odena · 2019
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Self-supervised gans via auxiliary rotation loss
Ting Chen, Xiaohua Zhai, Marvin Ritter, Mario Lucic, and Neil Houlsby · 2019
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High-fidelity image generation with fewer labels
Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, and Sylvain Gelly · 2019
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Image generation from small datasets via batch statistics adaptation
Atsuhiro Noguchi and Tatsuya Harada · 2019
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Rethinking the value of network pruning
Zhuang Liu, Mingjie Sun, Tinghui Zhou, Gao Huang, and Trevor Darrell · 2019
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The state of sparsity in deep neural networks
Trevor Gale, Erich Elsen, and Sara Hooker · 2019
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The difficulty of training sparse neural networks
Utku Evci, Fabian Pedregosa, Aidan Gomez, and Erich Elsen · 2019
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Jonathan Frankle, David J. Schwab, and Ari S. Morcos · 2020
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Winning the lottery with continuous sparsification
Pedro Savarese, Hugo Silva, and Michael Maire · 2020
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The sooner the better: Investigating structure of early winning lottery tickets, 2020
Shihui Yin, Kyu-Hyoun Kim, Jinwook Oh, Naigang Wang, Mauricio Serrano, Jae-Sun Seo, and Jungwook Choi · 2020
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Drawing early-bird tickets: Toward more efficient training of deep networks
Haoran You, Chaojian Li, Pengfei Xu, Yonggan Fu, Yue Wang, Xiaohan Chen, Richard G. Baraniuk, Zhangyang Wang, and Yingyan Lin · 2020
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Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Michael Carbin, and Zhangyang Wang · 2020
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Playing the lottery with rewards and multiple languages: lottery tickets in rl and nlp
Haonan Yu, Sergey Edunov, Yuandong Tian, and Ari S. Morcos · 2020
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The lottery ticket hypothesis for pre-trained bert networks
Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Zhangyang Wang, and Michael Carbin · 2020
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Earlybert: Efficient bert training via early-bird lottery tickets
Xiaohan Chen, Yu Cheng, Shuohang Wang, Zhe Gan, Zhangyang Wang, and Jingjing Liu · 2020
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Robust pre-training by adversarial contrastive learning
Ziyu Jiang, Tianlong Chen, Ting Chen, and Zhangyang Wang · 2020
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Once-for-all adversarial training: In-situ tradeoff between robustness and accuracy for free
Haotao Wang, Tianlong Chen, Shupeng Gui, Ting-Kuei Hu, Ji Liu, and Zhangyang Wang · 2020
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Ting-Kuei Hu, Tianlong Chen, Haotao Wang, and Zhangyang Wang · 2020
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Adversarial robustness: From self-supervised pre-training to fine-tuning
Tianlong Chen, Sijia Liu, Shiyu Chang, Yu Cheng, Lisa Amini, and Zhangyang Wang · 2020
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Adversarial examples improve image recognition
Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, Alan L Yuille, and Quoc V Le · 2020
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Freelb: Enhanced adversarial training for natural language understanding
Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun, Tom Goldstein, and Jingjing Liu · 2020
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Large-scale adversarial training for vision-and-language representation learning
Zhe Gan, Yen-Chun Chen, Linjie Li, Chen Zhu, Yu Cheng, and Jingjing Liu · 2020
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ContraGAN: Contrastive Learning for Conditional Image Generation
Minguk Kang and Jaesik Park · 2020
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Freeze discriminator: A simple baseline for fine-tuning gans
Sangwoo Mo, Minsu Cho, and Jinwoo Shin · 2020
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Minegan: effective knowledge transfer from gans to target domains with few images
Yaxing Wang, Abel Gonzalez-Garcia, David Berga, Luis Herranz, Fahad Shahbaz Khan, and Joost van de Weijer · 2020
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Big self-supervised models are strong semi-supervised learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Towards faster and stabilized gan training for high-fidelity few-shot image synthesis
Bingchen Liu, Yizhe Zhu, Kunpeng Song, and Ahmed Elgammal · 2020
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Enlightengan: Deep light enhancement without paired supervision
Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, and Zhangyang Wang · 2021
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Gans can play lottery tickets too
Xuxi Chen, Zhenyu Zhang, Yongduo Sui, and Tianlong Chen · 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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Long live the lottery: The existence of winning tickets in lifelong learning
Tianlong Chen, Zhenyu Zhang, Sijia Liu, Shiyu Chang, and Zhangyang Wang · 2021
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Good students play big lottery better
Haoyu Ma, Tianlong Chen, Ting-Kuei Hu, Chenyu You, Xiaohui Xie, and Zhangyang Wang · 2021
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Efficient lottery ticket finding: Less data is more
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A unified lottery ticket hypothesis for graph neural networks, 2021
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Adversarial feature augmentation and normalization for visual recognition
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