Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling

12 Mar 2020Tong CheRuixiang ZhangJascha Sohl-DicksteinHugo LarochelleLiam PaullYuan CaoYoshua Bengio

We show that the sum of the implicit generator log-density $\log p_g$ of a GAN with the logit score of the discriminator defines an energy function which yields the true data density when the generator is imperfect but the discriminator is optimal, thus making it possible to improve on the typical generator (with implicit density $p_g$). To make that practical, we show that sampling from this modified density can be achieved by sampling in latent space according to an energy-based model induced by the sum of the latent prior log-density and the discriminator output score... (read more)

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