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Generative Adversarial Networks (GANs) are a class of generative models used for various applications, but they have been known to suffer from the mode collapse problem, in which some modes of the target distribution are ignored by the generator.
Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
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Connectionist models of recognition memory: constraints imposed by learning and forgetting functions
Roger Ratcliff · 1990
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Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory
James L McClelland, Bruce L McNaughton, and Randall C O’Reilly · 1995
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The mnist database of handwritten digits
Yann LeCun · 1998
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Caltech-UCSD Birds 200
P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff, S. Belongie, and P. Perona · 2010
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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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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 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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Generative multi-adversarial networks
Ishan Durugkar, Ian Gemp, and Sridhar Mahadevan · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Unrolled generative adversarial networks
Luke Metz, Ben Poole, David Pfau, and Jascha Sohl-Dickstein · 2016
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Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
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Improved techniques for training gans, 2016
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Pathnet: Evolution channels gradient descent in super neural networks
Chrisantha Fernando, Dylan Banarse, Charles Blundell, Yori Zwols, David Ha, Andrei A Rusu, Alexander Pritzel, and Daan Wierstra · 2017
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C 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
Cited alongside, same era.
Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
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Dual discriminator generative adversarial nets
Tu Nguyen, Trung Le, Hung Vu, and Dinh Phung · 2017
Cited alongside, same era.
icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 2017
Cited alongside, same era.
Veegan: Reducing mode collapse in gans using implicit variational learning
Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
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Medical image synthesis with deep convolutional adversarial networks
Dong Nie, Roger Trullo, Jun Lian, Li Wang, Caroline Petitjean, Su Ruan, Qian Wang, and Dinggang Shen · 2018
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Deeproad: Gan-based metamorphic testing and input validation framework for autonomous driving systems
Mengshi Zhang, Yuqun Zhang, Lingming Zhang, Cong Liu, and Sarfraz Khurshid · 2018
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Seeing what a gan cannot generate
David Bau, Jun-Yan Zhu, Jonas Wulff, William Peebles, Hendrik Strobelt, Bolei Zhou, and Antonio Torralba · 2019
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Invertible convolutional flow
Mahdi Karami, Dale Schuurmans, Jascha Sohl-Dickstein, Laurent Dinh, and Daniel Duckworth · 2019
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Akash Srivastava, Lazar Valkov, Chris Russell, Michael U Gutmann, and Charles Sutton · 2017
Cited alongside, same era.
Adagan: Boosting generative models
Ilya O Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, and Bernhard Schölkopf · 2017
Cited alongside, same era.
Generative adversarial networks for noise reduction in low-dose ct
Jelmer M Wolterink, Tim Leiner, Max A Viergever, and Ivana Išgum · 2017
Cited alongside, same era.
Using simulation and domain adaptation to improve efficiency of deep robotic grasping
Konstantinos Bousmalis, Alex Irpan, Paul Wohlhart, Yunfei Bai, Matthew Kelcey, Mrinal Kalakrishnan, Laura Downs, Julian Ibarz, Peter Pastor, Kurt Konolige, et al · 2018
Cited alongside, same era.
Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2018
Cited alongside, same era.
Self-supervised gan to counter forgetting
Ting Chen, Xiaohua Zhai, and Neil Houlsby · 2018
Cited alongside, same era.
Mgan: Training generative adversarial nets with multiple generators
Quan Hoang, Tu Dinh Nguyen, Trung Le, and Dinh Phung · 2018
Cited alongside, same era.
A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Random path selection for continual learning
Jathushan Rajasegaran, Munawar Hayat, Salman H Khan, Fahad Shahbaz Khan, and Ling Shao · 2019
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Self-supervised gan: Analysis and improvement with multi-class minimax game
Ngoc-Trung Tran, Viet-Hung Tran, Bao-Ngoc Nguyen, Linxiao Yang, et al · 2019
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Generative adversarial network in medical imaging: A review
Xin Yi, Ekta Walia, and Paul Babyn · 2019
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Side-tuning: Network adaptation via additive side networks
Jeffrey O. Zhang, Alexander Sax, Amir Zamir, Leonidas J. Guibas, and Jitendra Malik · 2019
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Analyzing and improving the image quality of stylegan
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
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Pulse: Self-supervised photo upsampling via latent space exploration of generative models
Sachit Menon, Alexandru Damian, Shijia Hu, Nikhil Ravi, and Cynthia Rudin · 2020
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microbatchgan: Stimulating diversity with multi-adversarial discrimination
Gonçalo Mordido, Haojin Yang, and Christoph Meinel · 2020
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Rl-cyclegan: Reinforcement learning aware simulation-to-real
Kanishka Rao, Chris Harris, Alex Irpan, Sergey Levine, Julian Ibarz, and Mohi Khansari · 2020
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On catastrophic forgetting and mode collapse in gans
Hoang Thanh-Tung and Truyen Tran · 2020
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