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TH2.I: Generative Adversarial Networks

Session Type: Lecture
Time: Thursday, 7 May, 11:30 - 13:30
Location: On-Demand
Session Chairs: David Miller, Penn State University and Raviv Raich, Oregon State University
 
   TH2.I.1: UNIFIED SIGNAL COMPRESSION USING GENERATIVE ADVERSARIAL NETWORKS
         Bowen Liu; University of Michigan
         Ang Cao; University of Michigan
         Hun-Seok Kim; University of Michigan
 
   TH2.I.2: WIND: WASSERSTEIN INCEPTION DISTANCE FOR EVALUATING GENERATIVE ADVERSARIAL NETWORK PERFORMANCE
         Panagiotis Dimitrakopoulos; University of Ioannina
         Giorgos Sfikas; University of Ioannina
         Christophoros Nikou; University of Ioannina
 
   TH2.I.3: TRACE NORM GENERATIVE ADVERSARIAL NETWORKS FOR SENSOR GENERATION AND FEATURE EXTRACTION
         Shuai Zheng; Hitachi America Ltd
         Chetan Gupta; Hitachi America Ltd
 
   TH2.I.4: MAHALANOBIS DISTANCE BASED ADVERSARIAL NETWORK FOR ANOMALY DETECTION
         Yubo Hou; Institute for Infocomm Research
         Zhenghua Chen; Institute for Infocomm Research
         Min Wu; Institute for Infocomm Research
         Chuan-Sheng Foo; Institute for Infocomm Research
         Xiaoli Li; Institute for Infocomm Research
         Raed Shubair; Massachusetts Institute of Technology
 
   TH2.I.5: COMMUTING CONDITIONAL GANS FOR MULTI-MODAL FUSION
         Siddharth Roheda; North Carolina State University
         Hamid Krim; North Carolina State University
         Benjamin S. Riggan; University of Nebraska-Lincoln
 
   TH2.I.6: SEQUENCE-TO-SUBSEQUENCE LEARNING WITH CONDITIONAL GAN FOR POWER DISAGGREGATION
         Yungang Pan; Shandong University
         Ke Liu; Shandong University
         Zhaoyan Shen; Shandong University
         Xiaojun Cai; Shandong University
         Zhiping Jia; Shandong University