报告题目：Deep neural networks in image processing
报告人：香港浸会大学 台雪成 教授
报告时间：8：30-12：00, 13th December 2021.
报告地点：腾讯会议 (ID:976 873 620) 联系人：田信宽(Tel:18769092104)
Abstract：
In this talk, we present our recent research on using variational models as layers for deep neural networks (DNNs). We use image segmentation as an example. The technique can also be used for high dimensional data classification as well. Through this technique, we could integrate many well-know variational models for image segmentation into deep neural networks. The new networks will have the advantages of traditional DNNs. At the same time, the outputs from the new networks can also have many good properties of variational models for image segmentation. We will present some techniques to incorporate shape priors into the networks through the variational layers. We will show how to design networks with spatial regularization and volume preservation. We can also design networks with guarantee that the output shapes from the network for image segmentation must be convex shapes/star-shapes. It is numerically verified that these techniques can improve the performance when the true shapes satisfy these priors.
The ideas of these new networks is based on some relationship between the softmax function, the Potts models and the structure of traditional DNNs. We will explain this in detail which leads naturally to the newly designed networks.
This talk is based on joint works with Jun Liu, S. Luo and several other collaborators.
简介：
Professor Tai Xue-cheng’s research has been focused on numerical mathematics and computational mathematics. In recent years, he has worked mainly on image processing, data analysis and machine learning problems. He is using numerical partial differential techniques for the application of image processing and data classification and extended these techniques to other modern applications. Robust and accurate models and fast, stable algorithms are some of the main concerns in his research.
