stat946w18/AmbientGAN: Generative Models from Lossy Measurements

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Generative Adversarial Networks operate by simulating complex distributions but training them requires access to large amounts of high quality data. Ashish Bora, Eric Price and Alexandros G. Dimakis propose AmbientGAN as a way to recover the true underlying distribution from lossy data under certain conditions. Even when these conditions are not met the results from AmbientGAN still produce high quality results.

Problem Specification


AmbientGAN compared to GAN (AmbientGAN passes the generator output through a measurement function)


Open questions