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Generative Adversarial Networks (GANs) have shown compelling results in various tasks and applications in recent years.
Maximum entropy generators for energy-based models
Kumar, R.; Ozair, S.; Goyal, A.; Courville, A.; and Bengio, Y. 2019 · 1901
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Neural networks and physical systems with emergent collective computational abilities
Hopfield, J. J. 1982 · 1982
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Optimal perceptual inference
Hinton, G. E.; and Sejnowski, T. J. 1983 · 1983
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Making the world differentiable: On using fully recurrent self-supervised neural networks for dynamic reinforcement learning and planning in non-stationary environments
Schmidhuber, J. 1990 · 1990
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A possibility for implementing curiosity and boredom in model-building neural controllers
Schmidhuber, J. 1991 · 1991
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Generative adversarial networks are special cases of artificial curiosity (1990) and also closely related to predictability minimization (1991)
Schmidhuber, J. 2020 · 1991
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Nonparametric entropy estimation: An overview
Beirlant, J.; Dudewicz, E. J.; Györfi, L.; Van der Meulen, E. C.; et al. 1997 · 1997
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Nonlinear dimensionality reduction by locally linear embedding
Roweis, S. T.; and Saul, L. K. 2000 · 2000
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Markov Chain Monte Carlo and Gibbs sampling
Carlo, C. M. 2004 · 2004
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A tutorial on energy-based learning
LeCun, Y.; Chopra, S.; Hadsell, R.; Ranzato, M.; and Huang, F. 2006 · 2006
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Learning multiple layers of features from tiny images
Krizhevsky, A.; Hinton, G.; et al. 2009 · 2009
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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
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Conditional Generative Adversarial Nets
Mirza, M.; and Osindero, S. 2014 · 2014
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Deep learning face attributes in the wild
Liu, Z.; Luo, P.; Wang, X.; and Tang, X. 2015 · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A.; Metz, L.; and Chintala, S. 2016 · 2016
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Improved techniques for training GANs
Salimans, T.; Goodfellow, I.; Zaremba, W.; Cheung, V.; Radford, A.; and Chen, X. 2016 · 2016
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Rethinking the Inception architecture for computer vision
Szegedy, C.; Vanhoucke, V.; Ioffe, S.; Shlens, J.; and Wojna, Z. 2016 · 2016
Cited alongside, same era.
A theory of generative convnet
Xie, J.; Lu, Y.; Zhu, S.-C.; and Wu, Y. 2016 · 2016
Cited alongside, same era.
Wasserstein generative adversarial networks
Arjovsky, M.; Chintala, S.; and Bottou, L. 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
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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.
Least squares generative adversarial networks
Mao, X.; Li, Q.; Xie, H.; Lau, R. Y.; Wang, Z.; and Paul Smolley, S. 2017 · 2017
Cited alongside, same era.
Learning non-convergent non-persistent short-run MCMC toward energy-based model
Nijkamp, E.; Hill, M.; Zhu, S.-C.; and Wu, Y. N. 2019 · 2019
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Improving generalization and stability of generative adversarial networks
Thanh-Tung, H.; Tran, T.; and Venkatesh, S. 2019 · 2019
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Learning energy-based spatial-temporal generative convnets for dynamic patterns
Xie, J.; Zhu, S.-C.; and Wu, Y. N. 2019 · 2019
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Consistency regularization for generative adversarial networks
AZhang, H.; Zhang, Z.; Odena, A.; and Lee, H. 2020 · 2020
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A simple framework for contrastive learning of visual representations
Chen, T.; Kornblith, S.; Norouzi, M.; and Hinton, G. 2020 · 2020
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Generative adversarial networks
Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; and Bengio, Y. 2020 · 2020
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Conditional image synthesis with auxiliary classifier GANs
Odena, A.; Olah, C.; and Shlens, J. 2017 · 2017
Cited alongside, same era.
Synthesizing dynamic patterns by spatial-temporal generative convnet
Xie, J.; Zhu, S.-C.; and Nian Wu, Y. 2017 · 2017
Cited alongside, same era.
Energy-based generative adversarial network
Zhao, J.; Mathieu, M.; and LeCun, Y. 2017 · 2017
Cited alongside, same era.
Minibatch Gibbs sampling on large graphical models
De Sa, C.; Chen, V.; and Wong, W. 2018 · 2018
Cited alongside, same era.
Generative image inpainting with contextual attention
Yu, J.; Lin, Z.; Yang, J.; Shen, X.; Lu, X.; and Huang, T. S. 2018 · 2018
Cited alongside, same era.
Ae-ot: a new generative model based on extended semi-discrete optimal transport
An, D.; Guo, Y.; Lei, N.; Luo, Z.; Yau, S.-T.; and Gu, X. 2019 · 2019
Cited alongside, same era.
Later among the works it cites.
Your classifier is secretly an energy based model and you should treat it like one
Grathwohl, W.; Wang, K.-C.; Jacobsen, J.-H.; Duvenaud, D.; Norouzi, M.; and Swersky, K. 2020 · 2020
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Denoising diffusion probabilistic models
Ho, J.; Jain, A.; and Abbeel, P. 2020 · 2020
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Real or not real, that is the question
Xiangli, Y.; Deng, Y.; Dai, B.; Loy, C. C.; and Lin, D. 2020 · 2020
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Bounds all around: training energy-based models with bidirectional bounds
Geng, C.; Wang, J.; Gao, Z.; Frellsen, J.; and Hauberg, S. 2021 · 2021
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No MCMC for me: Amortized sampling for fast and stable training of energy-based models
Grathwohl, W. S.; Kelly, J. J.; Hashemi, M.; Norouzi, M.; Swersky, K.; and Duvenaud, D. 2021 · 2021
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AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation
Li, B.; Zhu, Y.; Wang, Y.; Lin, C.-W.; Ghanem, B.; and Shen, L. 2021 · 2021
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Liu, H.; Liang, H.; Hou, X.; Wu, H.; Liu, F.; and Shen, L. 2021 · 2021
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Overcoming Mode Collapse with Adaptive Multi Adversarial Training
Mangalam, K.; and Garg, R. 2021 · 2021
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Generative adversarial networks (GANs) challenges, solutions, and future directions
Saxena, D.; and Cao, J. 2021 · 2021
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