bet356体育在线育-亚洲版

bet356体育在线育
报告题目:

Learning Robust Imaging Models without Paired Data

报告人:

包承龙 助理教授(清华大学丘成桐数学科学中心)

报告时间:

报告地点:

腾讯会议ID:914-957-139

报告摘要:

The observations in practical imaging systems always contain complex noise such that classical approaches are difficult to obtain satisfactory results. In recent years, deep neural networks directly learned a map between the noisy and clean images based on the training on paired data. Despite its promising results in various tasks, collecting the training data is difficult and time-consuming in practice. In this talk, in the unpaired data regime, we will discuss our recent progress for building AI-aided robust models and their applications in image processing. Leveraging the Bayesian inference framework, our model combines classical mathematical modeling and deep neural networks to improve interpretability. Experimental results on various real datasets validate the advantages of the proposed methods.

Baidu
sogou
XML 地图 | Sitemap 地图