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Deep Learning: Hype or Hope?
时间:2016-06-27 11:06    点击:   所属单位:通信工程学院
讲座名称 Deep Learning: Hype or Hope?
讲座时间 2016-07-01 15:00:00
讲座地点 北校区新科技楼1012会议室
讲座人 Dr. C.-C. Jay Kuo
讲座人介绍 Dr. C.-C. Jay Kuo received his Ph.D. degree from the Massachusetts Institute of Technology in 1987. He is now with the University of Southern California (USC) as Director of the Media Communications Laboratory and Dean’s Professor in Electrical Engineering-Systems. His research interests are in the areas of digital media processing, compression, communication and networking technologies. Dr. Kuo was an IEEE Signal Processing Society Distinguished Lecturer in 2006, and the recipient of the Electronic Imaging Scientist of the Year Award in 2010 and the holder of the 2010-2011 Fulbright-Nokia Distinguished Chair in Information and Communications Technologies. Dr. Kuo is a Fellow of AAAS, IEEE and SPIE. Dr. Kuo has guided 130 students to their Ph.D. degrees and supervised 23 postdoctoral research fellows. He is a co-author of about 240 journal papers, 880 conference papers and 13 books.
讲座内容 Deep learning has received a lot of attention in recent years due to its superior performance in several speech recognition and computer vision benchmarking datasets. A deep network can learn features (called deep features) automatically from training data. To understand deep learning, the first step is to understand these deep features. After a review of the short history of applying deep learning to vision applications, I will use two quantitative metrics to shed lights on trained deep features. They are the Gaussian confusion measure (GCM) and the cluster purity measure (CPM). The GCM is used to identify the discriminative ability of an individual feature while the CPM is used to analyze the group discriminative ability of a set of deep features. It is confirmed by experiments that these two metrics accurately reflect the discriminative ability of trained deep features. Further studies with the metrics as tools reveal important insights into the deep network, such as its good detection performance of some object classes that were considered difficult in the past. Finally, I will explain my view to the deep learning methodology - its pros, cons and future perspectives.
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