Reducing the Representation Error of GAN Image Priors Using the Deep Decoder

23 Jan 2020Max DanielsPaul HandReinhard Heckel

Generative models, such as GANs, learn an explicit low-dimensional representation of a particular class of images, and so they may be used as natural image priors for solving inverse problems such as image restoration and compressive sensing. GAN priors have demonstrated impressive performance on these tasks, but they can exhibit substantial representation error for both in-distribution and out-of-distribution images, because of the mismatch between the learned, approximate image distribution and the data generating distribution... (read more)

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