#### Problem addressed: 
learning data distribution using non-parametric models

#### Summary: 
The authors propose learning generative model as playing an adversarial game, where one player is the generative model and the other is a discriminative model. The discriminative model learns to differentiate samples obtained from the generative model with true samples from the dataset, and the generative model tries to improve its data likelihood. They provide theoretical result that when one use such adversarial objective the global optimum of generative model will be the data distribution. Good generative performance is presented both quantitatively and qualitatively.

#### Novelty:
Treating learning generative model as playing a two player adversarial game, although such idea is kind of there in the training of energy based models, this paper is the first to successfully demonstrate this power.

#### Drawbacks:
As with any of the non-parametric generative models, the evaluation of data probability is not very easy.

#### Datasets:
MNIST, TFD, CIFAR-10 (only qualitative)

#### Additional remarks:
Presentation video available on cedar server

#### Presenter:
Yingbo Zhou