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 interfae for exmploring GAN latent spaces.
Delving deep into Generative Adversarial Networks (GANs):
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.
- HiOT06: (2006) A Fast Learning Algorithm for Deep Belief Nets. Neural Computation, 18(7), 1527–1554. DOI
- BlBo17: (2017) A neural parametric singing synthesizer. ArXiv:1704.03809 [Cs].
- HeWH16: (2016) A Powerful Generative Model Using Random Weights for the Deep Image Representation. In Advances in Neural Information Processing Systems.
- MoDH12: (2012) Acoustic Modeling Using Deep Belief Networks. IEEE Transactions on Audio, Speech, and Language Processing, 20(1), 14–22. DOI
- GaGh16: (2016) Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference. In 4th International Conference on Learning Representations (ICLR) workshop track.
- SLZC16: (2016) Composing Music with Grammar Argumented Neural Networks and Note-Level Encoding. ArXiv:1611.05416 [Cs].
- LGRN09: (2009) Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations. In Proceedings of the 26th Annual International Conference on Machine Learning (pp. 609–616). New York, NY, USA: ACM DOI
- KWKT15: (2015) Deep Convolutional Inverse Graphics Network. ArXiv:1503.03167 [Cs].
- DCSF15: (2015) Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks. ArXiv:1506.05751 [Cs].
- THSB17: (2017) Deep Probabilistic Programming. In ICLR.
- KaFe14: (2014) Deep Visual-Semantic Alignments for Generating Image Descriptions. ArXiv:1412.2306 [Cs].
- GDGR15: (2015) DRAW: A Recurrent Neural Network For Image Generation. ArXiv:1502.04623 [Cs].
- GoSS14: (2014) Explaining and Harnessing Adversarial Examples. ArXiv:1412.6572 [Cs, Stat].
- AGLM17: (2017) Generalization and Equilibrium in Generative Adversarial Nets (GANs). ArXiv:1703.00573 [Cs].
- BVVD15: (2015) Generating Sentences from a Continuous Space. ArXiv:1511.06349 [Cs].
- Grav13: (2013) Generating Sequences With Recurrent Neural Networks. ArXiv:1308.0850 [Cs].
- ThBe15: (2015) Generative Image Modeling Using Spatial LSTMs. ArXiv:1506.03478 [Cs, Stat].
- ZKSE16: (2016) Generative Visual Manipulation on the Natural Image Manifold. In Proceedings of European Conference on Computer Vision.
- PASA16: (2016) Improved generator objectives for GANs. In Advances in Neural Information Processing Systems 29.
- CDHS16: (2016) InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. In Advances in Neural Information Processing Systems 29 (pp. 2172–2180). Curran Associates, Inc.
- LCKF10: (2010) Kronecker Graphs: An Approach to Modeling Networks. Journal of Machine Learning Research, 11, 985–1042.
- Sala15: (2015) Learning Deep Generative Models. Annual Review of Statistics and Its Application, 2(1), 361–385. DOI
- Hint07: (2007) Learning multiple layers of representation. Trends in Cognitive Sciences, 11(10), 428–434. DOI
- DSTB14: (2014) Learning to Generate Chairs, Tables and Cars with Convolutional Networks. ArXiv:1411.5928 [Cs].
- ERRD17: (2017) Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders. In PMLR.
- BCCC17: (2017) Neural Causal Regularization under the Independence of Mechanisms Assumption. ArXiv:1702.02604 [Cs, Stat].
- MnGr14: (2014) Neural Variational Inference and Learning in Belief Networks. In Proceedings of The 31st International Conference on Machine Learning.
- GaGh15: (2015) On Modern Deep Learning and Variational Inference. In Advances in Approximate Bayesian Inference workshop, NIPS.
- OoKK16: (2016) Pixel Recurrent Neural Networks. ArXiv:1601.06759 [Cs].
- FSPW16: (2016) Sequential Neural Models with Stochastic Layers. In Advances in Neural Information Processing Systems 29 (pp. 2199–2207). Curran Associates, Inc.
- ReMW15: (2015) Stochastic Backpropagation and Approximate Inference in Deep Generative Models. In Proceedings of ICML.
- LoWe16: (2016) Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors. In arXiv preprint arXiv:1603.04733 (pp. 1708–1716).
- KrSS17: (2017) Structured Inference Networks for Nonlinear State Space Models. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (pp. 2101–2109).
- JeBV16: (2016) Texture Synthesis with Spatial Generative Adversarial Networks. In Advances in Neural Information Processing Systems 29.
- Coll08: (2008) The Analysis of Generative Music Programs. Organised Sound, 13(03), 237–248. DOI
- RaMC15: (2015) Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. In arXiv:1511.06434 [cs].
- PaVe14: (2014) Why does Deep Learning work? – A perspective from Group Theory. ArXiv:1412.6621 [Cs, Stat].