云南大学唐年胜教授学术报告

科研楼18号楼1102 

发布者:韩伟发布时间:2023-09-14浏览次数:432

报告题目: Deep expectation-maximization network for unsupervised image segmentation and clustering

时       间:202309月17日(星期0830

       点:科研楼18号楼1102 

       办:数学与统计学院

参加对象感兴趣的老师与学生 

 

报告摘要:Unsupervised learning, such as unsupervised image segmentation and clustering, are fundamentaltasks in image representation learning. In this paper, we design a deep expectation-maximization (DEM) network for unsupervised image segmentation and clustering. Itis based on the statistical modeling of image in its latentfeature space by Gaussian mixture model (GMM), implemented in a novel deep learning framework. Specifically, in the unsupervised setting, we design an auto-encoder network and an EM module over the image latent features, for jointly learning the image latent features and GMM model of the latent features in a single framework. To construct the EM-module, we unfold the iterative operations of EM algorithm and the online EM algorithm in fixed steps to be differentiable network blocks, plugged into the network to estimate the GMM parameters of the image latent features. The proposed network parameters can be end-to-end optimized using losses based on log-likelihood of GMM, entropy of Gaussian component assignment probabilities and image reconstruction error. Extensive experiments confirm that our proposed networks achieve favorable results compared with several state-of-the-art methods in unsupervised image segmentation and clustering.

 

报告人简介:唐年胜,云南大学教授,博士生导师,数学与统计学院院长。“国家杰出青年科学基金”获得者,教育部“长江学者”特聘教授,教育部“新世纪优秀人才”,国家百千万人才工程暨有突出贡献中青年科学家,享受国务院特殊津贴。国际统计学会推荐会员,国际数理统计学会会士,在Journal of the American Statistical Association(美国统计学会会刊)、Annals of  StatisticsBiomertrika等学术期刊发表论文170余篇,出版专著4部。