报告题目：High-Dimensional Data Recovery: When Deep Learning Meets Matrix/Tensor Decomposition

报告时间：2021-10-18  14:30-15:30

报告人：赵熙乐 教授  电子科技大学

腾讯会议ID：599 206 385

访问此链接进入会议，或添加至会议列表：https://meeting.tencent.com/dm/EuexcjfwaVHw

Abstract: Recently, low-rank tensor decomposition methods have received increasing attention for high-dimensional data recovery. However, only considering the low-rank structure of high-dimensional data is not sufficient for high-dimensional data recovery, especially for extremely complex imaging scenarios. In this talk, we will discuss how to bring into play the respective strengths of self-supervised learning and matrix/tensor decomposition for high-dimensional data recovery. Extensive numerical examples including inpainting, denoising, and snapshot compressed sensing are delivered to demonstrate the superiority of the suggested methods over state-of-the-art methods.