a资源吧视频在线观看

新闻动态
Interpretable Convolutional Neural Networks (CNNs) via Feedforward Design
时间:2019-06-10    浏览量:次

报告题目:Interpretable Convolutional Neural Networks (CNNs) via Feedforward Design

人:C.-C. Jay KuoUniversity of Southern California

报告时间:2019613日(星期四)上午1000

报告地点:重庆大学A区主教学大楼1811

报告摘要: Given a convolutional neural network (CNN) architecture, its network parameters are determined by backpropagation (BP) nowadays. The underlying mechanism remains to be a black-box after a large amount of theoretical investigation. In this talk, I describe a new interpretable and feedforward (FF) design with the LeNet-5 as an example. The FF-trained CNN is a data-centric approach that derives network parameters based on training data statistics layer by layer in one pass. To build the convolutional layers, we develop a new signal transform, called the Saab (Subspace approximation with adjusted bias) transform. The bias in filter weights is chosen to annihilate nonlinearity of the activation function. To build the fully-connected (FC) layers, we adopt a label-guided linear least squared regression (LSR) method. The classification performances of BP- and FF-trained CNNs on the MNIST and the CIFAR-10 datasets are compared. The computational complexity of the FF design is significantly lower than the BP design and, therefore, the FF-trained CNN is ideal for mobile/edge computing. We also comment on the relationship between BP and FF designs by examining the cross-entropy values at nodes of intermediate layers.

主讲人介绍: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 Distinguished Professor of Electrical Engineering and Computer Science. His research interests are in the areas of media processing, compression and understanding. Dr. Kuo was the Editor-in-Chief for the IEEE Trans. on Information Forensics and Security in 2012-2014. Dr. Kuo is a Fellow of AAAS, IEEE and SPIE. He has guided 150 students to their Ph.D. degrees and supervised 29 postdoctoral research fellows. Dr. Kuo is a co-author of 280 journal papers, 920 conference papers and 14 books. Dr. Kuo received the 2016 IEEE Computer Society Taylor L. Booth Education Award, the 2016 IEEE Circuits and Systems Society John Choma Education Award, the 2016 IS&T Raymond C. Bowman Award, the 2017 IEEE Leon K. Kirchmayer Graduate Teaching Award, the 2017 IEEE Signal Processing Society Education Award, and the 2019 IEEE Computer Society Edward J. McCluskey Technical Achievement Award.

 


a毛大片免费在线观看_国产大片免费观看软件_手机免费观看大片 男女刺激大片免费观看_免费一级特黄大片韩国_成一级人大片在线观看 成一级人大片在线观看_高清大片免费网站污_美国大片视频免费观看 手机看免费大片完整版_日本高清大片免费_毛大片视频在线观看 日本大片免费播放网站_大片在线视频免播放器_免费一级的大片在线 男女性色大片免费看_看免费男女大片用什么软件_免费一级特黄大片韩国 午夜大片免费观看30分钟_免费一级特黄大片国内_日本高清大片免费放 成在线人免63特黄大片i_日本高清大片免费放_毛大片视频在线观看 美国一级大片国语版_手机免费观看大片_免费生活大片 日本一级大片在线播放_生活大片一级直播_69大片视频免费观看视频