报告题目：Deep image prior for inverse problems: acceleration and probabilistic treatment

报告时间：2023-06-21  15:30 - 16:30

报告人：金邦梯，香港中文大学

报告地点：理学院东北楼302

Abstract: Since its first proposal in 2018, deep image prior has emerged as a very powerful unsupervised deep learning technique for solving inverse problems. The approach has demonstrated very encouraging empirical success in image denoising, deblurring, super-resolution etc. However, there are also several known drawbacks of the approach, notably high computational expense. In this talk, we describe some of our efforts: we propose to accelerate the training process by pretraining on synthetic dataset and further we propose a novel probabilistic treatment of deep image prior to facilitate uncertainty quantification.