Improving Multi-Manifold GANs with a Learned Noise Prior

ICLR 2020 Anonymous

Generative adversarial networks (GANs) learn to map samples from a noise distribution to a chosen data distribution. Recent work has demonstrated that GANs are consequently sensitive to, and limited by, the shape of the noise distribution... (read more)

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