Fetching the paper…
Reading the bibliography…
Despite remarkable performance in producing realistic samples, Generative Adversarial Networks (GANs) often produce low-quality samples near low-density regions of the data manifold, e.g., samples of minor groups.
Pattern recognition and machine learning
Christopher M Bishop · 2006
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.
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.
Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
Earlier work this paper cites.
Deepface: Closing the gap to human-level performance in face verification
Yaniv Taigman, Ming Yang, Marc’Aurelio Ranzato, and Lior Wolf · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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.
Deep multi-scale video prediction beyond mean square error
Michael Mathieu, Camille Couprie, and Yann LeCun · 2015
Earlier work this paper cites.
Learning discriminative reconstructions for unsupervised outlier removal
Yan Xia, Xudong Cao, Fang Wen, Gang Hua, and Jian Sun · 2015
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.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2016
Earlier work this paper cites.
Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Earlier work this paper cites.
Training region-based object detectors with online hard example mining
Abhinav Shrivastava, Abhinav Gupta, and Ross Girshick · 2016
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna · 2016
Earlier work this paper cites.
Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Earlier work this paper cites.
Began: Boundary equilibrium generative adversarial networks
David Berthelot, Thomas Schumm, and Luke Metz · 2017
Earlier work this paper cites.
Active bias: Training more accurate neural networks by emphasizing high variance samples
Haw-Shiuan Chang, Erik Learned-Miller, and Andrew McCallum · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2017
Cited alongside, same era.
Jae Hyun Lim and Jong Chul Ye · 2017
Cited alongside, same era.
Unrolled generative adversarial networks
Luke Metz, Ben Poole, David Pfau, and Jascha Sohl-Dickstein · 2017
Cited alongside, same era.
Variational approaches for auto-encoding generative adversarial networks
Mihaela Rosca, Balaji Lakshminarayanan, David Warde-Farley, and Shakir Mohamed · 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.
A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 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.
Mining gold samples for conditional gans
Sangwoo Mo, Chiheon Kim, Sungwoong Kim, Minsu Cho, and Jinwoo Shin · 2019
Later among the works it cites.
Self-supervised GAN: analysis and improvement with multi-class minimax game
Ngoc-Trung Tran, Viet-Hung Tran, Ngoc-Bao Nguyen, Linxiao Yang, and Ngai-Man Cheung · 2019
Later among the works it cites.
Metropolis-hastings generative adversarial networks
Ryan Turner, Jane Hung, Eric Frank, Yunus Saatchi, and Jason Yosinski · 2019
Later among the works it cites.
Logan: Latent optimisation for generative adversarial networks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Hierarchical implicit models and likelihood-free variational inference
Dustin Tran, Rajesh Ranganath, and David M. Blei · 2017
Cited alongside, same era.
Anomaly detection with robust deep autoencoders
Chong Zhou and Randy C. Paffenroth · 2017
Cited alongside, same era.
Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
Cited alongside, same era.
Banach wasserstein GAN
Jonas Adler and Sebastian Lunz · 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.
Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
Cited alongside, same era.
Which training methods for gans do actually converge?
Lars M. Mescheder, Andreas Geiger, and Sebastian Nowozin · 2018
Cited alongside, same era.
Yan Wu, Jeff Donahue, David Balduzzi, Karen Simonyan, and Timothy Lillicrap · 2019
Later among the works it cites.
Making convolutional networks shift-invariant again
Richard Zhang · 2019
Later among the works it cites.
Instance selection for gans
Terrance DeVries, Michal Drozdzal, and Graham W. Taylor · 2020
Later among the works it cites.
Subsampling generative adversarial networks: Density ratio estimation in feature space with softplus loss
Xin Ding, Z Jane Wang, and William J Welch · 2020
Later among the works it cites.
Analyzing and improving the image quality of stylegan
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
Later among the works it cites.
Mimicry: Towards the reproducibility of gan research
Kwot Sin Lee and Christopher Town · 2020
Later among the works it cites.
PacGAN: The power of two samples in generative adversarial networks
Zinan Lin, Ashish Khetan, Giulia Fanti, and Sewoong Oh · 2020
Later among the works it cites.
Top-k training of gans: Improving gan performance by throwing away bad samples
Samarth Sinha, Zhengli Zhao, Anirudh Goyal ALIAS PARTH GOYAL, Colin A Raffel, and Augustus Odena · 2020
Later among the works it cites.
Bridging the gap between f-gans and wasserstein gans
Jiaming Song and Stefano Ermon · 2020
Later among the works it cites.
Dataset cartography: Mapping and diagnosing datasets with training dynamics
Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A. Smith, and Yejin Choi · 2020
Later among the works it cites.
Xiaoxia Wu, Ethan Dyer, and Behnam Neyshabur · 2020
Later among the works it cites.
Improving GAN training with probability ratio clipping and sample reweighting
Yue Wu, Pan Zhou, Andrew Gordon Wilson, Eric P. Xing, and Zhiting Hu · 2020
Later among the works it cites.
Inclusive gan: Improving data and minority coverage in generative models
Ning Yu, Ke Li, Peng Zhou, Jitendra Malik, Larry Davis, and Mario Fritz · 2020
Later among the works it cites.