Technical Program

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ARS-09: Explainable Machine Learning for Computer Vision

Interactive Q&A Time: Tuesday, 27 October, 14:30 - 14:55
Virtual Session: View on Virtual Platform
Session Chair: Gene Cheung, York University
 
 ARS-09.1: HRINET: ALTERNATIVE SUPERVISION NETWORK FOR HIGH-RESOLUTION CT IMAGE INTERPOLATION
         Jiawei Li; University of Ottawa
         Jae Chul Koh; the Korea University Anam Hospital
         Won-Sook Lee; University of Ottawa
 
 ARS-09.2: INTERPRETABLE SYNTHETIC REDUCED NEAREST NEIGHBOR: AN EXPECTATION MAXIMIZATION APPROACH
         Pooya Tavallali; University of California, Merced
         Peyman Tavallali; Independet Researcher
         Mohammad Reza Khosravi; Shiraz University of Technology
         Mukesh Singhal; University of California, Merced
 
 ARS-09.3: AIM-NET: BRING IMPLICIT EULER TO NETWORK DESIGN
         Qiongwen Yuan; Wuhan University
         Jingwei He; Wuhan University
         Lei Yu; Wuhan University
         Gang Zheng; INRIA Lille
 
 ARS-09.4: MULTI-SCALE EXPLAINABLE FEATURE LEARNING FOR PATHOLOGICAL IMAGE ANALYSIS USING CONVOLUTIONAL NEURAL NETWORKS
         Kazuki Uehara; National Institute of Advanced Industrial Science and Technology (AIST)
         Masahiro Murakawa; National Institute of Advanced Industrial Science and Technology (AIST)
         Hirokazu Nosato; National Institute of Advanced Industrial Science and Technology (AIST)
         Hidenori Sakanashi; National Institute of Advanced Industrial Science and Technology (AIST)
 
 ARS-09.5: SALIENCY-DRIVEN CLASS IMPRESSIONS FOR FEATURE VISUALIZATION OF DEEP NEURAL NETWORKS
         Sravanti Addepalli; Indian Institute of Science
         Dipesh Tamboli; Indian Institute of Technology Bombay
         Venkatesh Babu Radhakrishnan; Indian Institute of Science
         Biplab Banerjee; Indian Institute of Technology Bombay
 
 ARS-09.6: VARIATIONAL ENCODER-BASED RELIABLE CLASSIFICATION
         Chitresh Bhushan; GE Research
         Zhaoyuan Yang; GE Research
         Nurali Virani; GE Research
         Naresh Iyer; GE Research
 
 ARS-09.7: HOUGHENCODER: NEURAL NETWORK ARCHITECTURE FOR DOCUMENT IMAGE SEMANTIC SEGMENTATION
         Alexander Sheshkus; Smart Engines Service LLC
         Dmitry Nikolaev; Institute for Information Transmission Problems (Kharkevich Institute) Russian Academy of Sciences
         Vladimir L Arlazarov; Federal Research Center “Computer Science and Control” of Russian Academy of Sciences