The Living Thing / Notebooks : Adversarial learning

Game theory meets learning. Hip, especially in combination with deep learning.

I don’t know anything about it.

Sanjeev Arora gives a cogent intro He also suggests a link with learning theory.

Implementations

Refs

ArBo17
Arjovsky, M., & Bottou, L. (2017) Towards Principled Methods for Training Generative Adversarial Networks. arXiv:1701.04862 [Stat].
ArCB17
Arjovsky, M., Chintala, S., & Bottou, L. (2017) Wasserstein GAN. arXiv:1701.07875 [Cs, Stat].
AGLM17
Arora, S., Ge, R., Liang, Y., Ma, T., & Zhang, Y. (2017) Generalization and Equilibrium in Generative Adversarial Nets (GANs). arXiv:1703.00573 [Cs].
GPMX14
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., … Bengio, Y. (2014) Generative Adversarial Networks. arXiv:1406.2661 [Cs, Stat].
GoSS14
Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014) Explaining and Harnessing Adversarial Examples. arXiv:1412.6572 [Cs, Stat].
JeBV16
Jetchev, N., Bergmann, U., & Vollgraf, R. (2016) Texture Synthesis with Spatial Generative Adversarial Networks. In Advances in Neural Information Processing Systems 29.
LSLW15
Larsen, A. B. L., Sønderby, S. K., Larochelle, H., & Winther, O. (2015) Autoencoding beyond pixels using a learned similarity metric. arXiv:1512.09300 [Cs, Stat].
PASA16
Poole, B., Alemi, A. A., Sohl-Dickstein, J., & Angelova, A. (2016) Improved generator objectives for GANs. In Advances in Neural Information Processing Systems 29.
RaMC15
Radford, A., Metz, L., & Chintala, S. (2015) Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv:1511.06434 [Cs].