MisGAN: Learning from Incomplete Data with Generative Adversarial Networks

ICLR 2019 Steven Cheng-Xian LiBo JiangBenjamin Marlin

Generative adversarial networks (GANs) have been shown to provide an effective way to model complex distributions and have obtained impressive results on various challenging tasks. However, typical GANs require fully-observed data during training... (read more)

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