Tempered Adversarial Networks

ICML 2018 Mehdi S. M. SajjadiGiambattista ParascandoloArash MehrjouBernhard Schölkopf

Generative adversarial networks (GANs) have been shown to produce realistic samples from high-dimensional distributions, but training them is considered hard. A possible explanation for training instabilities is the inherent imbalance between the networks: While the discriminator is trained directly on both real and fake samples, the generator only has control over the fake samples it produces since the real data distribution is fixed by the choice of a given dataset... (read more)

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