Approximability of Discriminators Implies Diversity in GANs

ICLR 2019 Yu BaiTengyu MaAndrej Risteski

While Generative Adversarial Networks (GANs) have empirically produced impressive results on learning complex real-world distributions, recent works have shown that they suffer from lack of diversity or mode collapse. The theoretical work of Arora et al. suggests a dilemma about GANs' statistical properties: powerful discriminators cause overfitting, whereas weak discriminators cannot detect mode collapse... (read more)

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