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SAMPLe Seminar

January 31, 2020 - 1:30pm

Speaker: Omar DeGuchy
Title: Deep Image Prior
Abstract: Review of a paper by Dmirty Ulyanov and his team. "Deep convolutional networks have become a popular tool for image generation and restoration. Generally, their excellent performance is imputed to their ability to learn realistic image priors from a large number of example images. In this paper, we show that, on the contrary, the structure of a generator network is sufficient to capture a great deal of low-level image statistics prior to any learning. In order to do so, we show that a randomly-initialized neural network can be used as a handcrafted prior with excellent results in standard inverse problems such as denoising, superresolution, and inpainting."


ACS 362B