In this paper, we propose to equip Generative Adversarial Networks with the ability to produce direct energy estimates for samples.Specifically, we propose a flexible adversarial training framework, and prove this framework not only ensures the generator converges to the true data distribution, but also enables the discriminator to retain the density information at the global optimal. We derive the analytic form of the induced solution, and analyze the properties. In order to make the proposed framework trainable in practice, we introduce two effective approximation techniques. Empirically, the experiment results closely match our theoretical analysis, verifying the discriminator is able to recover the energy of data distribution.
@article{arxiv.1702.01691,
title = {Calibrating Energy-based Generative Adversarial Networks},
author = {Zihang Dai and Amjad Almahairi and Philip Bachman and Eduard Hovy and Aaron Courville},
journal= {arXiv preprint arXiv:1702.01691},
year = {2017}
}