Mathematical analysis of loss function of GAN and its loss function variants
Abstract:Generative adversarial networks (GANs) have turned up as the most widely used approaches for creating realistic samples. They're the effective latent variable models for learning complex real distributions. However, despite their enormous success and popularity, the process of training GANs remains challenging and suffers from a number of failures. These failures include mode collapse where the generator generates the same set of output for different inputs which finally leads to loss of diversity; non-converg… Show more
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