Self-supervised Deep Learning for Solving Inverse Problems in Imaging

报告题目：Self-supervised Deep Learning for Solving Inverse Problems in Imaging

报告人：纪辉 教授（新加坡国立大学）

主持人：沈超敏 副教授

报告时间：2023年6月13日（星期二）10:00-11:00

报告地点：腾讯会议（会议号：644-344-658）

报告摘要：

Deep learning has proved to be a powerful tool in many domains, including inverse problems imaging sciences. However, most existing successful deep learning solutions to inverse imaging problems are based on supervised learning, which requires many ground-truth images for training a deep neural network (DNN). This prerequisite on training datasets limits their applicability in data-limited domains, such as medicine and science. In this talk, we will introduce a series of works on self-supervised learning for solving inverse imaging problems. Our approach teaches a DNN to predict images from their noisy and partial measurements without seeing any related truth image, which is achieved by neuralization of Bayesian inference with DNN-based over-parametrization of images. Surprisingly, our proposed self-supervised method can compete well against supervised learning methods in many real-world tasks.

报告人简介：  

Dr. Ji Hui received his Ph.D. in Computer Science from the University of Maryland at College Park in 2006. He currently is a Professor at the Department of Mathematics and serves as the Deputy Director of the Centre for Data Science and Machine Learning at NUS. His research interests lie in wavelet theory, computational vision, imaging science, and machine learning.