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Calibrating Energy-based Generative Adversarial Networks
[article]
2017
arXiv
pre-print
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
arXiv:1702.01691v2
fatcat:piljftrdufbixcvwbzhplha6re