Ntroducing a Dversarial D Ropout in G Enera - Tive M Ulti - a Dversarial N Etworks
نویسندگان
چکیده
We propose to extend the original generative adversarial networks (GANs) framework to multiple discriminators and omit, or dropout, the feedback of each discriminator with same probability at the end of each batch. Our approach forces the generator to not rely on a given discriminator to learn how to produce realistic looking samples, but, instead, on a dynamic ensemble of adversaries. This promotes variety of the generated samples, leading to a richer generator less prone to mode collapsing. We show preliminary results on MNIST and Fashion-MNIST that sustain our claims.
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تاریخ انتشار 2018