Dmitry Ulyanov - Deep Image Prior

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. Furthermore, the same prior can be used to invert deep neural representations to diagnose them and to restore images based on flash-no flash input pairs.

Apart from its diverse applications, our approach highlights the inductive bias captured by standard generator network architectures. It also bridges the gap between two very popular families of image restoration methods: learning-based methods using deep convolutional networks and learning-free methods based on handcrafted image priors such as self-similarity.

Dmitry Ulyanov received his major in Machine Learning at Moscow State University and now studies for his Ph.D. degree at Skoltech Institute. His supervisors are Victor Lempitsky and Andrea Vedaldi and his work is mostly focused on image synthesis and generative models. Dmitry also serves as teaching assistant at Deep Learning class at Skoltech and Yandex's School of Data Analysis. He worked in Yandex and had an internship at Google. Dmitry is a prize winner in more than 10 Data Science contests and runs class about competitive Data Science on Coursera.