Global Guarantees for Enforcing Deep Generative Priors by Empirical Risk

22 May 2017 Paul Hand Vladislav Voroninski

We examine the theoretical properties of enforcing priors provided by generative deep neural networks via empirical risk minimization. In particular we consider two models, one in which the task is to invert a generative neural network given access to its last layer and another in which the task is to invert a generative neural network given only compressive linear observations of its last layer... (read more)

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