A Bayesian Perspective on the Deep Image Prior

Zezhou Cheng, Matheus Gadelha, Subhransu Maji, Daniel Sheldon; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 5443-5451

Abstract


The deep image prior was recently introduced as a prior for natural images. It represents images as the output of a convolutional network with random inputs. For "inference", gradient descent is performed to adjust network parameters to make the output match observations. This approach yields good performance on a range of image reconstruction tasks. We show that the deep image prior is asymptotically equivalent to a stationary Gaussian process prior in the limit as the number of channels in each layer of the network goes to infinity, and derive the corresponding kernel. This informs a Bayesian approach to inference. We show that by conducting posterior inference using stochastic gradient Langevin dynamics we avoid the need for early stopping, which is a drawback of the current approach, and improve results for denoising and impainting tasks. We illustrate these intuitions on a number of 1D and 2D signal reconstruction tasks.

Related Material


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[bibtex]
@InProceedings{Cheng_2019_CVPR,
author = {Cheng, Zezhou and Gadelha, Matheus and Maji, Subhransu and Sheldon, Daniel},
title = {A Bayesian Perspective on the Deep Image Prior},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}