Technical Program

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MP.PB: Neural and Deep Networks

Session Type: Poster
Time: Monday, September 23, 14:10 - 15:58
Location: Room 101 (1F) - Poster Area B
Session Chair: Dong Tian, InterDigital
 
 MP.PB.1: REINFORCING THE ROBUSTNESS OF A DEEP NEURAL NETWORK TO ADVERSARIAL EXAMPLES BY USING COLOR QUANTIZATION OF TRAINING IMAGE DATA
         Shuntaro Miyazato; University of Tokyo
         Xueting Wang; University of Tokyo
         Toshihiko Yamasaki; University of Tokyo
         Kiyoharu Aizawa; University of Tokyo
 
 MP.PB.2: MULTI-LOSS-AWARE CHANNEL PRUNING OF DEEP NETWORKS
         Yiming Hu; Institute of Automation, Chinese Academy of Sciences; University of Chinese Academy of Sciences
         Siyang Sun; Institute of Automation, Chinese Academy of Sciences; University of Chinese Academy of Sciences
         Jianquan Li; Institute of Automation, Chinese Academy of Sciences; University of Chinese Academy of Sciences
         Jiagang Zhu; Institute of Automation, Chinese Academy of Sciences; University of Chinese Academy of Sciences
         Xingang Wang; Institute of Automation, Chinese Academy of Sciences
         Qingyi Gu; Institute of Automation, Chinese Academy of Sciences
 
 MP.PB.3: DCT BASED INFORMATION-PRESERVING POOLING FOR DEEP NEURAL NETWORKS
         Yuhao Xu; University of Tokyo
         Hideki Nakayama; University of Tokyo
 
 MP.PB.4: TRAINING ACCURATE BINARY NEURAL NETWORKS FROM SCRATCH
         Joseph Bethge; Hasso Plattner Institute, University of Potsdam
         Haojin Yang; Hasso Plattner Institute, University of Potsdam
         Christoph Meinel; Hasso Plattner Institute, University of Potsdam
 
 MP.PB.5: CREATING 3D BOUNDING BOX HYPOTHESES FROM DEEP NETWORK SCORE-MAPS
         Lin Guo; Oklahoma State University
         Guoliang Fan; Oklahoma State University
         Weihua Sheng; Oklahoma State University
 
 MP.PB.6: ADVERSARIAL NOISE LAYER: REGULARIZE NEURAL NETWORK BY ADDING NOISE
         Zhonghui You; Peking University
         Jinmian Ye; University of Electronic Science and Technology of China
         Kunming Li; Australian National University
         Zenglin Xu; University of Electronic Science and Technology of China
         Ping Wang; Peking University
 
 MP.PB.7: CLUSTER REGULARIZED QUANTIZATION FOR DEEP NETWORKS COMPRESSION
         Yiming Hu; Institute of Automation, Chinese Academy of Sciences; University of Chinese Academy of Sciences
         Jianquan Li; Institute of Automation, Chinese Academy of Sciences; University of Chinese Academy of Sciences
         Xianlei Long; Institute of Automation, Chinese Academy of Sciences; University of Chinese Academy of Sciences
         Shenhua Hu; Institute of Automation, Chinese Academy of Sciences; University of Chinese Academy of Sciences
         Jiagang Zhu; Institute of Automation, Chinese Academy of Sciences; University of Chinese Academy of Sciences
         Xingang Wang; Institute of Automation, Chinese Academy of Sciences
         Qingyi Gu; Institute of Automation, Chinese Academy of Sciences
 
 MP.PB.8: NEURAL NETWORK MAXIMIZING ORDINALLY SUPERVISED MULTI-VIEW CANONICAL CORRELATION FOR DETERIORATION LEVEL ESTIMATION
         Keisuke Maeda; Hokkaido University
         Sho Takahashi; Hokkaido University
         Takahiro Ogawa; Hokkaido University
         Miki Haseyama; Hokkaido University