Von Mises-Fisher Mixture Model-based Deep learning and its Application to Face Verification
讲座名称：
Von Mises-Fisher Mixture Model-based Deep learning and its Application to Face Verification
讲座时间：
2018-05-18
讲座人：
Liming Chen
形式：
校区：
兴庆校区
实践学分：
讲座内容：
讲座题目：Von Mises-Fisher Mixture Model-based Deep learning and its Application to Face Verification
讲座时间：5月18号下午2点半到4点
讲座地点：管理学院313会议室
讲座人：Prof. Liming Chen
讲座摘要：A number of pattern recognition tasks, e.g. , face verification, can be boiled down to classification or clustering of unit length directional feature vectors whose distance can be simply computed by their angle. In this paper, we propose the von Mises-Fisher (vMF) mixture model as the theoretical foundation for an effective deep-learning of such directional features and derive a novel vMF Mixture Loss and its corresponding vMF deep features. The proposed vMF features learning achieves a discriminative learning, i.e. , compacting the instances of the same class while increasing the distance of instances from different classes, and subsumes a number of loss functions or deep learning practice, e.g. , normalization. The experiments carried out on face verification using 4 different challenging face datasets, i.e. , LFW, IJB-A, YouTube faces and CACD, show the effectiveness of the proposed approach, which displays very competitive and state-of-the-art results.
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