2025.06.03 Colloquia Seminars
| Speaker |
孟德宇教授，西安交通大学 |
| Title |
Continuous Representation-Induced Regularization Methods for Multi-Dimensional Data Recovery |
| Time |
2025.06.10 10:00-11:00 |
| Venue |
N204 |
| Abstract |
Most classical regularization-based methods for multi-dimensional imaging data recovery can solely represent multi-dimensional discrete data on meshgrid, which hinders their potential applicability in many scenarios beyond meshgrid. To break this barrier, we propose a series of continuous functional representation methods, which can continuously represent data beyond meshgrid with powerful representation abilities. Specifically, the suggested continuous representation manner, which maps an arbitrary coordinate to the corresponding value, can continuously represent data in an infinite real space. Such an ameliorated representation regime always facilitates better efficiency, accuracy, and wider range of available domains (e.g., non-meshgrid data) of regularization based methods. In this talk, we will introduce how to revolutionize the conventional low-rank, TV, non-local self-similarity regulation methods into their continuous ameliorations, i.e., Low-Rank Tensor Function Representation (termed as LRTFR), neural domain TV (termed as NeurTV), and Continuous Representation-based NonLocal method (termed as CRNL), respectively. We will also show extensive multi-dimensional data recovery applications arising from image processing (like image inpainting and denoising), machine learning (like hyperparameter optimization), and computer graphics (like point cloud upsampling) to validate the favorable performances of our method for continuous representation. |
| Affiliation |
孟德宇教授，西安交通大学教授，博导。长江学者，国家“万人计划”青年拔尖人才，中国工业与应用数学学会副理事长，任西安交大大数据算法与分析技术国家工程实验室机器学习教研室负责人。共发表论文100余篇，其中包括IEEE汇刊论文40余篇，CCF A类会议论文40余篇，谷歌引用万余次。目前主要聚焦于元学习、可解释深度学习等机器学习与计算机视觉领域的基础研究问题。 | |
