Game theory meets learning. Hip, especially in combination with deep learning.
I don’t know anything about it. Something about training two systems together to both generate and classify examples of a phenomenon of interest.
Sanjeev Arora gives a cogent intro He also suggests a link with learning theory. Here is a spreadsheet interface for exploring GAN latent spaces.
See also Delving deep into Generative Adversarial Networks, a “curated, quasi-exhaustive list of state-of-the-art publications and resources about Generative Adversarial Networks (GANs) and their applications.”
The GAN Zoo, “A list of all named GANs!”
To discover: precise relationship of deep GANS with, e.g. adversarial training in games and bandit problems.
Arjovsky, Martin, Soumith Chintala, and Léon Bottou. 2017. “Wasserstein Generative Adversarial Networks.” In International Conference on Machine Learning, 214–23. http://proceedings.mlr.press/v70/arjovsky17a.html.
Arora, Sanjeev, Rong Ge, Yingyu Liang, Tengyu Ma, and Yi Zhang. 2017. “Generalization and Equilibrium in Generative Adversarial Nets (GANs),” March. http://arxiv.org/abs/1703.00573.
Bahadori, Mohammad Taha, Krzysztof Chalupka, Edward Choi, Robert Chen, Walter F. Stewart, and Jimeng Sun. 2017. “Neural Causal Regularization Under the Independence of Mechanisms Assumption,” February. http://arxiv.org/abs/1702.02604.
Blaauw, Merlijn, and Jordi Bonada. 2017. “A Neural Parametric Singing Synthesizer,” April. http://arxiv.org/abs/1704.03809.
Bowman, Samuel R., Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Jozefowicz, and Samy Bengio. 2015. “Generating Sentences from a Continuous Space,” November. http://arxiv.org/abs/1511.06349.
Chen, Xi, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel. 2016. “InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets.” In Advances in Neural Information Processing Systems 29, edited by D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, R. Garnett, and R. Garnett, 2172–80. Curran Associates, Inc. http://papers.nips.cc/paper/6399-infogan-interpretable-representation-learning-by-information-maximizing-generative-adversarial-nets.pdf.
Denton, Emily, Soumith Chintala, Arthur Szlam, and Rob Fergus. 2015. “Deep Generative Image Models Using a Laplacian Pyramid of Adversarial Networks,” June. http://arxiv.org/abs/1506.05751.
Dosovitskiy, Alexey, Jost Tobias Springenberg, Maxim Tatarchenko, and Thomas Brox. 2014. “Learning to Generate Chairs, Tables and Cars with Convolutional Networks,” November. http://arxiv.org/abs/1411.5928.
Engel, Jesse, Cinjon Resnick, Adam Roberts, Sander Dieleman, Douglas Eck, Karen Simonyan, and Mohammad Norouzi. 2017. “Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders.” In PMLR. http://arxiv.org/abs/1704.01279.
Fraccaro, Marco, Sø ren Kaae Sø nderby, Ulrich Paquet, and Ole Winther. 2016. “Sequential Neural Models with Stochastic Layers.” In Advances in Neural Information Processing Systems 29, edited by D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, 2199–2207. Curran Associates, Inc. http://papers.nips.cc/paper/6039-sequential-neural-models-with-stochastic-layers.pdf.
Frühstück, Anna, Ibraheem Alhashim, and Peter Wonka. 2019. “TileGAN: Synthesis of Large-Scale Non-Homogeneous Textures,” April. https://doi.org/10.1145/3306346.3322993.
Gal, Yarin, and Zoubin Ghahramani. 2015. “On Modern Deep Learning and Variational Inference.” In Advances in Approximate Bayesian Inference Workshop, NIPS.
———. 2016. “Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference.” In 4th International Conference on Learning Representations (ICLR) Workshop Track. http://arxiv.org/abs/1506.02158.
Goodfellow, Ian J., Jonathon Shlens, and Christian Szegedy. 2014. “Explaining and Harnessing Adversarial Examples,” December. http://arxiv.org/abs/1412.6572.
Goodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. “Generative Adversarial Nets.” In Advances in Neural Information Processing Systems 27, edited by Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, 2672–80. NIPS’14. Cambridge, MA, USA: Curran Associates, Inc. http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf.
Gregor, Karol, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, and Daan Wierstra. 2015. “DRAW: A Recurrent Neural Network for Image Generation,” February. http://arxiv.org/abs/1502.04623.
Gulrajani, Ishaan, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville. 2017. “Improved Training of Wasserstein GANs,” March. http://arxiv.org/abs/1704.00028.
He, Kun, Yan Wang, and John Hopcroft. 2016. “A Powerful Generative Model Using Random Weights for the Deep Image Representation.” In Advances in Neural Information Processing Systems. http://arxiv.org/abs/1606.04801.
Hinton, Geoffrey E. 2007. “Learning Multiple Layers of Representation.” Trends in Cognitive Sciences 11 (10): 428–34. https://doi.org/10.1016/j.tics.2007.09.004.
Jetchev, Nikolay, Urs Bergmann, and Roland Vollgraf. 2016. “Texture Synthesis with Spatial Generative Adversarial Networks.” In Advances in Neural Information Processing Systems 29. http://arxiv.org/abs/1611.08207.
Krishnan, Rahul G., Uri Shalit, and David Sontag. 2017. “Structured Inference Networks for Nonlinear State Space Models.” In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2101–9. http://arxiv.org/abs/1609.09869.
Kulkarni, Tejas D., Will Whitney, Pushmeet Kohli, and Joshua B. Tenenbaum. 2015. “Deep Convolutional Inverse Graphics Network,” March. http://arxiv.org/abs/1503.03167.
Lee, Honglak, Roger Grosse, Rajesh Ranganath, and Andrew Y. Ng. 2009. “Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations.” In Proceedings of the 26th Annual International Conference on Machine Learning, 609–16. ICML ’09. New York, NY, USA: ACM. https://doi.org/10.1145/1553374.1553453.
Louizos, Christos, and Max Welling. 2016. “Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors.” In arXiv Preprint arXiv:1603.04733, 1708–16. http://arxiv.org/abs/1603.04733.
Mnih, Andriy, and Karol Gregor. 2014. “Neural Variational Inference and Learning in Belief Networks.” In Proceedings of the 31st International Conference on Machine Learning. http://www.jmlr.org/proceedings/papers/v32/mnih14.html.
Mohamed, A. r, G. E. Dahl, and G. Hinton. 2012. “Acoustic Modeling Using Deep Belief Networks.” IEEE Transactions on Audio, Speech, and Language Processing 20 (1): 14–22. https://doi.org/10.1109/TASL.2011.2109382.
Oord, Aäron van den, Nal Kalchbrenner, and Koray Kavukcuoglu. 2016. “Pixel Recurrent Neural Networks,” January. http://arxiv.org/abs/1601.06759.
Panaretos, Victor M., and Yoav Zemel. 2019. “Statistical Aspects of Wasserstein Distances.” Annual Review of Statistics and Its Application 6 (1): 405–31. https://doi.org/10.1146/annurev-statistics-030718-104938.
Pascual, Santiago, Joan Serrà, and Antonio Bonafonte. 2019. “Towards Generalized Speech Enhancement with Generative Adversarial Networks,” April. http://arxiv.org/abs/1904.03418.
Pfau, David, and Oriol Vinyals. 2016. “Connecting Generative Adversarial Networks and Actor-Critic Methods,” October. http://arxiv.org/abs/1610.01945.
Poole, Ben, Alexander A. Alemi, Jascha Sohl-Dickstein, and Anelia Angelova. 2016. “Improved Generator Objectives for GANs.” In Advances in Neural Information Processing Systems 29. http://arxiv.org/abs/1612.02780.
Radford, Alec, Luke Metz, and Soumith Chintala. 2015. “Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.” In. http://arxiv.org/abs/1511.06434.
Rezende, Danilo Jimenez, Shakir Mohamed, and Daan Wierstra. 2015. “Stochastic Backpropagation and Approximate Inference in Deep Generative Models.” In Proceedings of ICML. http://arxiv.org/abs/1401.4082.
Salakhutdinov, Ruslan. 2015. “Learning Deep Generative Models.” Annual Review of Statistics and Its Application 2 (1): 361–85. https://doi.org/10.1146/annurev-statistics-010814-020120.
Sun, Zheng, Jiaqi Liu, Zewang Zhang, Jingwen Chen, Zhao Huo, Ching Hua Lee, and Xiao Zhang. 2016. “Composing Music with Grammar Argumented Neural Networks and Note-Level Encoding,” November. http://arxiv.org/abs/1611.05416.
Theis, Lucas, and Matthias Bethge. 2015. “Generative Image Modeling Using Spatial LSTMs,” June. http://arxiv.org/abs/1506.03478.
Tran, Dustin, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, and David M. Blei. 2017. “Deep Probabilistic Programming.” In ICLR. http://arxiv.org/abs/1701.03757.
Zhu, Jun-Yan, Philipp Krähenbühl, Eli Shechtman, and Alexei A. Efros. 2016. “Generative Visual Manipulation on the Natural Image Manifold.” In Proceedings of European Conference on Computer Vision. http://arxiv.org/abs/1609.03552.