Fetching the paper…
Reading the bibliography…
While Generative Adversarial Networks (GANs) are fundamental to many generative modelling applications, they suffer from numerous issues.
Theory of Reproducing Kernels
Nachman Aronszajn · 1950
Earlier work this paper cites.
Self-Organization in a Perceptual Network
Ralph Linsker · 1988
Earlier work this paper cites.
Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem
Michael McCloskey and Neal J Cohen · 1989
Earlier work this paper cites.
The MNIST Database of Handwritten Digits
Yann LeCun · 1998
Earlier work this paper cites.
The Information Bottleneck Method
Naftali Tishby, Fernando C Pereira, and William Bialek · 2000
Earlier work this paper cites.
The IM Algorithm: A Variational Approach to Information Maximization
David Barber and Felix V Agakov · 2003
Earlier work this paper cites.
Estimation of Entropy and Mutual Information
Liam Paninski · 2003
Earlier work this paper cites.
Imagenet: A Large-Scale Hierarchical Image Database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning Multiple Layers of Features from Tiny Images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
An Analysis of Single-Layer Networks in Unsupervised Feature Learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
Earlier work this paper cites.
A Kernel Two-Sample Test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
Earlier work this paper cites.
Representation Learning: A Review and New Perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
Earlier work this paper cites.
Generative Adversarial Nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Deep Learning Face Attributes in the Wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Earlier work this paper cites.
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
Earlier work this paper cites.
Mode Regularized Generative Adversarial Networks
Tong Che, Yanran Li, Athul Paul Jacob, Yoshua Bengio, and Wenjie Li · 2016
Earlier work this paper cites.
Infogan: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
Earlier work this paper cites.
NIPS 2016 Tutorial: Generative Adversarial Networks
Ian Goodfellow · 2016
Earlier work this paper cites.
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Unrolled Generative Adversarial Networks
Luke Metz, Ben Poole, David Pfau, and Jascha Sohl-Dickstein · 2016
Earlier work this paper cites.
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
Cited alongside, same era.
Improved Techniques for Training Gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Cited alongside, same era.
Rethinking the Inception Architecture for Computer Vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Cited alongside, same era.
Improving Generative Adversarial Networks with Denoising Feature Matching
David Warde-Farley and Yoshua Bengio · 2016
Cited alongside, same era.
An Online Learning Approach to Generative Adversarial Networks
Paulina Grnarova, Kfir Y Levy, Aurelien Lucchi, Thomas Hofmann, and Andreas Krause · 2017
Cited alongside, same era.
Assessing Generative Models via Precision and Recall
Mehdi SM Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, and Sylvain Gelly · 2018
Later among the works it cites.
Dist-Gan: An Improved GAN Using Distance Constraints
Ngoc-Trung Tran, Tuan-Anh Bui, and Ngai-Man Cheung · 2018
Later among the works it cites.
https://github.com/pfnet-research/sngan_projection
GANs with Spectral Normalization and Projection Discriminator · 2019
Later among the works it cites.
Learning Representations by Maximizing Mutual Information Across Views
Philip Bachman, R Devon Hjelm, and William Buchwalter · 2019
Later among the works it cites.
Self-Supervised Gans via Auxiliary Rotation Loss
Ting Chen, Xiaohua Zhai, Marvin Ritter, Mario Lucic, and Neil Houlsby · 2019
Later among the works it cites.
A Domain Agnostic Measure for Monitoring and Evaluating GANs
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Least Squares Generative Adversarial Networks
Xudong Mao, Qing Li, Haoran Xie, Raymond Y.K. Lau, Zhen Wang, and Stephen Paul Smolley · 2017
Cited alongside, same era.
Conditional Image Synthesis with Auxiliary Classifier Gans
Augustus Odena, Christopher Olah, and Jonathon Shlens · 2017
Cited alongside, same era.
Veegan: Reducing Mode Collapse in Gans Using Implicit Variational Learning
Akash Srivastava, Lazar Valkov, Chris Russell, Michael U Gutmann, and Charles Sutton · 2017
Cited alongside, same era.
Mikołaj Bińkowski, Dougal J Sutherland, Michael Arbel, and Arthur Gretton · 2018
Cited alongside, same era.
Paulina Grnarova, Kfir Y Levy, Aurelien Lucchi, Nathanael Perraudin, Ian Goodfellow, Thomas Hofmann, and Andreas Krause · 2019
Later among the works it cites.
Data-Efficient Image Recognition with Contrastive Predictive Coding
Olivier J Hénaff, Ali Razavi, Carl Doersch, SM Eslami, and Aaron van den Oord · 2019
Later among the works it cites.
A Mutual Information Maximization Perspective of Language Representation Learning
Lingpeng Kong, Cyprien de Masson d’Autume, Wang Ling, Lei Yu, Zihang Dai, and Dani Yogatama · 2019
Later among the works it cites.
A Large-scale Study on Regularization and Normalization in GANs
Karol Kurach, Mario Lučić, Xiaohua Zhai, Marcin Michalski, and Sylvain Gelly · 2019
Later among the works it cites.
Improved Precision and Recall Metric for Assessing Generative Models
Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2019
Later among the works it cites.
Greedy InfoMax for Biologically Plausible Self-Supervised Representation Learning
Sindy Löwe, Peter O’Connor, and Bastiaan S Veeling · 2019
Later among the works it cites.
Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis
Qi Mao, Hsin-Ying Lee, Hung-Yu Tseng, Siwei Ma, and Ming-Hsuan Yang · 2019
Later among the works it cites.
Open Questions About Generative Adversarial Networks
Augustus Odena · 2019
Later among the works it cites.
Wasserstein Dependency Measure for Representation Learning
Sherjil Ozair, Corey Lynch, Yoshua Bengio, Aaron Van den Oord, Sergey Levine, and Pierre Sermanet · 2019
Later among the works it cites.
Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2019
Later among the works it cites.
Improving GAN with Neighbors Embedding and Gradient Matching
Ngoc-Trung Tran, Tuan-Anh Bui, and Ngai-Man Cheung · 2019
Later among the works it cites.
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
Later among the works it cites.
An Improved Self-supervised GAN via Adversarial Training
Ngoc-Trung Tran, Viet-Hung Tran, Ngoc-Bao Nguyen, and Ngai-Man Cheung · 2019
Later among the works it cites.
On Mutual Information Maximization for Representation Learning
Michael Tschannen, Josip Djolonga, Paul K Rubenstein, Sylvain Gelly, and Mario Lucic · 2019
Later among the works it cites.
A Simple Framework for Contrastive Learning of Visual Representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Closest in time.
Mimicry: Towards the Reproducibility of GAN Research
Kwot Sin Lee and Christopher Town · 2020
Closest in time.