Time: Tuesday, April 17, 2018, 2:00pm @ MTH3206
Speaker: Soledad Villar (NYU)
Title: Stable denoising with generative networks
Abstract: It has been experimentally established that deep neural networks can be used to produce good generative models for real world data. It has also been established that such generative models can be exploited to solve classical inverse problems like compressed sensing and super resolution. In this work we focus on the classical signal processing problem of image denoising. We propose a theoretical setting that uses spherical harmonics to identify what mathematical properties of the activation functions will allow signal denoising with local methods.
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