报告题目：New Continuous Representation for Multi-Dimensional Data Recovery Beyond Meshgrid

主讲人：赵熙乐教授

单位：电子科技大学

时间：2月5日9:00

腾讯ID：170-171-300

摘要：Classical low-rank tensor representations can only represent data on meshgrid, which hinders their potential applicability in many scenarios beyond meshgrid. To break this barrier, we propose a low-rank tensor function representation (LRTFR), which can continuously represent data beyond meshgrid with infinite resolution. Specifically, the suggested tensor function, which maps an arbitrary coordinate to the corresponding value, can continuously represent data in an infinite real space. Parallel to discrete tensors, we develop two fundamental concepts for tensor functions, i.e., the tensor function rank and low-rank tensor function factorization. We theoretically justify that both low-rank and smooth regularizations are harmoniously unified in the LRTFR, which leads to high effectiveness and efficiency for data continuous representation. Extensive multi-dimensional data recovery applications arising from image processing (image inpainting and denoising), machine learning (hyperparameter optimization), and computer graphics (point cloud upsampling) substantiate the superiority and versatility of our method as compared with state-of-the-art methods. Especially, the experiments beyond the original meshgrid resolution (hyperparameter optimization) or even beyond meshgrid (point cloud upsampling) validate the favorable performances of our method for continuous representation.

简介：赵熙乐，电子科技大学教授、博导，入选四川省天府青城计划和四川省学术和技术带头人后备人选。撰写Elsevier出版社和科学出版社出版的学术专著章节2章，第一/通讯在权威SIAM系列期刊(SIIMS和SISC)和IEEE系列期刊(TPAMI、TIP、TNNLS、TCYB、TCVST、TCI和TGRS)、ISPRS及计算机学会A类会议CVPR和AAAI等发表研究工作。研究成果获四川省科技进步一等奖两项(自然科学类、科技进步类)、第一、二届川渝科技学术大会优秀论文一等奖，计算数学会青年优秀论文竞赛二等奖。主持国自然面上项目、国自然青年项目、华为项目。