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Global Guarantees for Enforcing Deep Generative Priors by Empirical Risk
[article]
2018
arXiv
pre-print
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. We establish that in both cases, in suitable regimes of network layer sizes and a randomness
arXiv:1705.07576v3
fatcat:7zvv7paszbcubnoabbntizunvu