ICCV19: Oral Session 1.2A - Architectures, Multi-Task Learning, Domain Adaptation

ICCV 2019

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Link to indexed video of session: https://conftube.com/video/9Sx2qWKGzlc 1. Exploring Randomly Wired Neural Networks for Image Recognition Saining Xie, Alexander Kirillov, Ross Girshick, Kaiming He https://conftube.com/video/9Sx2qWKGzlc?tocitem=2 2. Progressive Differentiable Architecture Search: Bridging the Depth Gap Between Search and Evaluation Xin Chen, Lingxi Xie, Jun Wu, Qi Tian https://conftube.com/video/9Sx2qWKGzlc?tocitem=12 3. Multinomial Distribution Learning for Effective Neural Architecture Search Xiawu Zheng, Rongrong Ji, Lang Tang, Baochang Zhang, Jianzhuang Liu, Qi Tian https://conftube.com/video/9Sx2qWKGzlc?tocitem=23 4. Searching for MobileNetV3 Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, Hartwig Adam https://conftube.com/video/9Sx2qWKGzlc?tocitem=32 5. Data-Free Quantization Through Weight Equalization and Bias Correction Markus Nagel, Mart van Baalen, Tijmen Blankevoort, Max Welling https://conftube.com/video/9Sx2qWKGzlc?tocitem=40 6. A Camera That CNNs: Towards Embedded Neural Networks on Pixel Processor Arrays Laurie Bose, Jianing Chen, Stephen J. Carey, Piotr Dudek, Walterio Mayol-Cuevas https://conftube.com/video/9Sx2qWKGzlc?tocitem=53 7. Knowledge Distillation via Route Constrained Optimization Xiao Jin, Baoyun Peng, Yichao Wu, Yu Liu, Jiaheng Liu, Ding Liang, Junjie Yan, Xiaolin Hu https://conftube.com/video/9Sx2qWKGzlc?tocitem=69 8. Distillation-Based Training for Multi-Exit Architectures Mary Phuong, Christoph H. Lampert https://conftube.com/video/9Sx2qWKGzlc?tocitem=77 9. Similarity-Preserving Knowledge Distillation Frederick Tung, Greg Mori https://conftube.com/video/9Sx2qWKGzlc?tocitem=88 10. Many Task Learning With Task Routing Gjorgji Strezoski, Nanne van Noord, Marcel Worring https://conftube.com/video/9Sx2qWKGzlc?tocitem=94 11. Stochastic Filter Groups for Multi-Task CNNs: Learning Specialist and Generalist Convolution Kernels Felix J.S. Bragman, Ryutaro Tanno, Sebastien Ourselin, Daniel C. Alexander, Jorge Cardoso https://conftube.com/video/9Sx2qWKGzlc?tocitem=100 12. Transferability and Hardness of Supervised Classification Tasks Anh T. Tran, Cuong V. Nguyen, Tal Hassner https://conftube.com/video/9Sx2qWKGzlc?tocitem=109 13. Moment Matching for Multi-Source Domain Adaptation Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, Bo Wang https://conftube.com/video/9Sx2qWKGzlc?tocitem=116 14. Unsupervised Domain Adaptation via Regularized Conditional Alignment Safa Cicek, Stefano Soatto https://conftube.com/video/9Sx2qWKGzlc?tocitem=124 15. Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain Adaptation Ruijia Xu, Guanbin Li, Jihan Yang, Liang Lin https://conftube.com/video/9Sx2qWKGzlc?tocitem=136 16. UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation Jogendra Nath Kundu, Nishank Lakkakula, R. Venkatesh Babu https://conftube.com/video/9Sx2qWKGzlc?tocitem=148 17. Episodic Training for Domain Generalization Da Li, Jianshu Zhang, Yongxin Yang, Cong Liu, Yi-Zhe Song, Timothy M. Hospedales https://conftube.com/video/9Sx2qWKGzlc?tocitem=155 18. Domain Adaptation for Structured Output via Discriminative Patch Representations Yi-Hsuan Tsai, Kihyuk Sohn, Samuel Schulter, Manmohan Chandraker https://conftube.com/video/9Sx2qWKGzlc?tocitem=162 19. Semi-Supervised Learning by Augmented Distribution Alignment Qin Wang, Wen Li, Luc Van Gool https://conftube.com/video/9Sx2qWKGzlc?tocitem=172 20. S4L: Self-Supervised Semi-Supervised Learning Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov, Lucas Beyer https://conftube.com/video/9Sx2qWKGzlc?tocitem=180

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