Learning the probability distribution of high-dimensional data is a challenging problem. To solve this problem, we formulate a deep energy adversarial network (DEAN), which casts the energy model learned from real data into an optimization of a goodness-of-fit (GOF) test statistic. DEAN can be interpreted as a GOF game between two generative networks, where one explicit generative network learns an energy-based distribution that fits the real data, and the other implicit generative network is trained by minimizing a GOF test statistic between the energy-based distribution and the generated data, such that the underlying distribution of the generated data is close to the energy-based distribution. We design a two-level alternative optimization procedure to train the explicit and implicit generative networks, such that the hyper-parameters can also be automatically learned. Experimental results show that DEAN achieves high quality generations compared to the state-of-the-art approaches.
|Original language||English (US)|
|Title of host publication||33rd Annual Conference on Neural Information Processing Systems, NeurIPS 2019|
|Publisher||Neural information processing systems foundation|
|State||Published - Jan 1 2019|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-09
Acknowledgements: This work was supported in part by National Natural Science Foundation of China (No. 61703396), the CCF-Tencent Open Fund and Shenzhen Government (GJHZ20180419190732022).