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Recent work has increased the performance of Generative Adversarial Networks (GANs) by enforcing a consistency cost on the discriminator.
Unsupervised Data Augmentation for Consistency Training
Xie, Q.; Dai, Z.; Hovy, E.; Luong, M.-T.; and Le, Q. V. 2019 · 1904
Earlier work this paper cites.
S 4 {}^{\mbox{4}} L: Self-Supervised Semi-Supervised Learning
Zhai, X.; Oliver, A.; Kolesnikov, A.; and Beyer, L. 2019 · 1905
Earlier work this paper cites.
Your Local GAN: Designing Two Dimensional Local Attention Mechanisms for Generative Models
Daras, G.; Odena, A.; Zhang, H.; and Dimakis, A. G. 2019 · 1911
Earlier work this paper cites.
LOGAN: Latent Optimisation for Generative Adversarial Networks
Wu, Y.; Donahue, J.; Balduzzi, D.; Simonyan, K.; and Lillicrap, T. 2019 · 1912
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Training generative adversarial networks with limited data
Karras, T.; Aittala, M.; Hellsten, J.; Laine, S.; Lehtinen, J.; and Aila, T. 2020 · 2006
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Differentiable augmentation for data-efficient gan training
Zhao, S.; Liu, Z.; Lin, J.; Zhu, J.-Y.; and Han, S. 2020a · 2006
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Image Augmentations for GAN Training
Zhao, Z.; Zhang, Z.; Chen, T.; Singh, S.; and Zhang, H. 2020b · 2006
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Optimal transport: old and new , volume 338
Villani, C. 2008 · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, A.; Hinton, G.; et al. 2009 · 2009
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Learning with pseudo-ensembles
Bachman, P.; Alsharif, O.; and Precup, D. 2014 · 2014
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Generative adversarial nets
Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; and Bengio, Y. 2014 · 2014
Earlier work this paper cites.
Don’t let your Discriminator be fooled
Zhou, B.; and Krähenbühl, P. 2019 · 2014
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A.; Metz, L.; and Chintala, S. 2015 · 2015
Earlier work this paper cites.
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O.; Deng, J.; Su, H.; Krause, J.; Satheesh, S.; Ma, S.; Huang, Z.; Karpathy, A.; Khosla, A.; Bernstein, M.; Berg, A. C.; and Fei-Fei, L. 2015 · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Cited alongside, same era.
Temporal ensembling for semi-supervised learning
Laine, S.; and Aila, T. 2016 · 2016
Cited alongside, same era.
Regularization with stochastic transformations and perturbations for deep semi-supervised learning
Sajjadi, M.; Javanmardi, M.; and Tasdizen, T. 2016 · 2016
Cited alongside, same era.
Improved techniques for training gans
Salimans, T.; Goodfellow, I.; Zaremba, W.; Cheung, V.; Radford, A.; and Chen, X. 2016 · 2016
Cited alongside, same era.
Arjovsky, M.; Chintala, S.; and Bottou, L. 2017 · 2017
Cited alongside, same era.
Is generator conditioning causally related to gan performance?
Odena, A.; Buckman, J.; Olsson, C.; Brown, T. B.; Olah, C.; Raffel, C.; and Goodfellow, I. 2018 · 2018
Later among the works it cites.
Realistic evaluation of deep semi-supervised learning algorithms
Oliver, A.; Odena, A.; Raffel, C. A.; Cubuk, E. D.; and Goodfellow, I. 2018 · 2018
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Improving the improved training of wasserstein gans: A consistency term and its dual effect
Wei, X.; Gong, B.; Liu, Z.; Lu, W.; and Wang, L. 2018 · 2018
Later among the works it cites.
Generating Natural Adversarial Examples
Zhao, Z.; Dua, D.; and Singh, S. 2018 · 2018
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MixMatch: A Holistic Approach to Semi-Supervised Learning
Berthelot, D.; Carlini, N.; Goodfellow, I. J.; Papernot, N.; Oliver, A.; and Raffel, C. 2019 · 2019
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DeVries, T.; and Taylor, G. W. 2017 · 2017
Cited alongside, same era.
Improved training of wasserstein gans
Gulrajani, I.; Ahmed, F.; Arjovsky, M.; Dumoulin, V.; and Courville, A. C. 2017 · 2017
Cited alongside, same era.
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Heusel, M.; Ramsauer, H.; Unterthiner, T.; Nessler, B.; and Hochreiter, S. 2017 · 2017
Cited alongside, same era.
On convergence and stability of gans
Kodali, N.; Abernethy, J.; Hays, J.; and Kira, Z. 2017 · 2017
Cited alongside, same era.
Lim, J. H.; and Ye, J. C. 2017 · 2017
Cited alongside, same era.
Conditional image synthesis with auxiliary classifier gans
Odena, A.; Olah, C.; and Shlens, J. 2017 · 2017
Cited alongside, same era.
Deep and hierarchical implicit models
Tran, D.; Ranganath, R.; and Blei, D. M. 2017 · 2017
Cited alongside, same era.
Large Scale GAN Training for High Fidelity Natural Image Synthesis
Brock, A.; Donahue, J.; and Simonyan, K. 2019 · 2019
Later among the works it cites.
A large-scale study on regularization and normalization in GANs
Kurach, K.; Lucic, M.; Zhai, X.; Michalski, M.; and Gelly, S. 2019 · 2019
Later among the works it cites.
Open questions about generative adversarial networks
Odena, A. 2019 · 2019
Later among the works it cites.
Diversity-sensitive conditional generative adversarial networks
Yang, D.; Hong, S.; Jang, Y.; Zhao, T.; and Lee, H. 2019 · 2019
Later among the works it cites.
Self-attention generative adversarial networks
Zhang, H.; Goodfellow, I.; Metaxas, D.; and Odena, A. 2019 · 2019
Later among the works it cites.
Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples
Sinha, S.; Zhao, Z.; Goyal, A.; Raffel, C.; and Odena, A. 2020 · 2020
Closest in time.
Consistency Regularization for Generative Adversarial Networks
Zhang, H.; Zhang, Z.; Odena, A.; and Lee, H. 2020 · 2020
Closest in time.
Stabilizing training of generative adversarial networks through regularization
Roth, K.; Lucchi, A.; Nowozin, S.; and Hofmann, T. 2017 · 2028
Closest in time.