北洋智算论坛 | 第二十五讲预告
“北洋智算论坛”第二十五讲
讲座时间
2019 年 1 月 17 日（星期四）10 时 00 分
讲座地点
天津大学（北洋园校区）
55B202
主讲人
王井东
讲座题目：
Interleaved Group Convolutions for Efficient Deep Neural Network
讲座内容：
Eliminating the redundancy in convolution kernels has been attracting increasing interests for designing efficient convolutional neural network architectures with three goals: small model, fast computation, and high accuracy. Existing solutions include low-precision kernels, structured sparse kernels, low-rank kernels, and the product of low-rank kernels. In this talk, I will introduce a novel framework: Interleaved Group Convolution (IGC), which uses the product of structured sparse kernels to compose a dense convolution kernel. It is a drop-in replacement of normal convolution and can be applied to any networks that depend on convolution. I present the complementary condition and the balance condition to guide the design, obtaining a balance between three aspects: model size, computation complexity and classification performance. I will show empirical and theoretic justification of the advantage of the proposed approach over Xception and MobileNet.
主讲人简介：
Jingdong Wang is a Senior Researcher with the Visual Computing Group, Microsoft Research, Beijing, China. His areas of current interest include efficient CNN architecture design, person re-identification, human pose estimation, semantic segmentation, large-scale indexing, and salient object detection. He has authored one book and 100+ papers in top conferences and prestigious international journals in computer vision, multimedia, and machine learning. His paper was selected into the Best Paper Finalist at the ACM MM 2015. He has shipped a dozen of technologies to Microsoft products, including Bing search, Cognitive service, and XiaoIce Chatbot. Dr. Wang is an Associate Editor of IEEE TPAMI, IEEE TCSVT and IEEE TMM. He was an Area Chair or a Senior Program Committee Member of top conferences, such as CVPR, ICCV, ECCV, AAAI, IJCAI, and ACM Multimedia. He is an ACM Distinguished Member and a Fellow of the IAPR. His homepage is https://jingdongwang2017.github.io/ .
