报告题目: Deep expectation-maximization network for unsupervised image segmentation and clustering
时 间：2023年09月17日（星期日）08：30
地 点：科研楼18号楼1102
主 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.