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Exploring Set Similarity for Dense Self-supervised Representation Learning


Jul 19, 2021
Zhaoqing Wang, Qiang Li, Guoxin Zhang, Pengfei Wan, Wen Zheng, Nannan Wang, Mingming Gong, Tongliang Liu

* 14 pages, 9 figures 

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Kernel Mean Estimation by Marginalized Corrupted Distributions


Jul 10, 2021
Xiaobo Xia, Shuo Shan, Mingming Gong, Nannan Wang, Fei Gao, Haikun Wei, Tongliang Liu


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Revisiting Knowledge Distillation: An Inheritance and Exploration Framework


Jul 01, 2021
Zhen Huang, Xu Shen, Jun Xing, Tongliang Liu, Xinmei Tian, Houqiang Li, Bing Deng, Jianqiang Huang, Xian-Sheng Hua

* Accepted by CVPR 2021 

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Understanding and Improving Early Stopping for Learning with Noisy Labels


Jun 30, 2021
Yingbin Bai, Erkun Yang, Bo Han, Yanhua Yang, Jiatong Li, Yinian Mao, Gang Niu, Tongliang Liu

* 19 pages, 5 figures 

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Probabilistic Margins for Instance Reweighting in Adversarial Training


Jun 15, 2021
Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama

* 17 pages, 4 figures 

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PI-GNN: A Novel Perspective on Semi-Supervised Node Classification against Noisy Labels


Jun 14, 2021
Xuefeng Du, Tian Bian, Yu Rong, Bo Han, Tongliang Liu, Tingyang Xu, Wenbing Huang, Junzhou Huang

* 16 pages, 3 figures 

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TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation


Jun 11, 2021
Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, William K. Cheung, James T. Kwok


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KRADA: Known-region-aware Domain Alignment for Open World Semantic Segmentation


Jun 11, 2021
Chenhong Zhou, Feng Liu, Chen Gong, Tongliang Liu, Bo Han, William Cheung


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Adversarial Robustness through the Lens of Causality


Jun 11, 2021
Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, Kun Zhang


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Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training


Jun 10, 2021
Dawei Zhou, Nannan Wang, Xinbo Gao, Bo Han, Jun Yu, Xiaoyu Wang, Tongliang Liu


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Towards Defending against Adversarial Examples via Attack-Invariant Features


Jun 09, 2021
Dawei Zhou, Tongliang Liu, Bo Han, Nannan Wang, Chunlei Peng, Xinbo Gao


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Reliable Adversarial Distillation with Unreliable Teachers


Jun 09, 2021
Jianing Zhu, Jiangchao Yao, Bo Han, Jingfeng Zhang, Tongliang Liu, Gang Niu, Jingren Zhou, Jianliang Xu, Hongxia Yang


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Understanding (Generalized) Label Smoothing when Learning with Noisy Labels


Jun 09, 2021
Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Yang Liu

* Under Review 

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Understanding (Generalized) Label Smoothing whenLearning with Noisy Labels


Jun 08, 2021
Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Yang Liu

* Under Review 

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Instance Correction for Learning with Open-set Noisy Labels


Jun 01, 2021
Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama


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Sample Selection with Uncertainty of Losses for Learning with Noisy Labels


Jun 01, 2021
Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama


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NoiLIn: Do Noisy Labels Always Hurt Adversarial Training?


May 31, 2021
Jingfeng Zhang, Xilie Xu, Bo Han, Tongliang Liu, Gang Niu, Lizhen Cui, Masashi Sugiyama


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Estimating Instance-dependent Label-noise Transition Matrix using DNNs


May 27, 2021
Shuo Yang, Erkun Yang, Bo Han, Yang Liu, Min Xu, Gang Niu, Tongliang Liu


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Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition


Apr 22, 2021
Lie Ju, Xin Wang, Lin Wang, Tongliang Liu, Xin Zhao, Tom Drummond, Dwarikanath Mahapatra, Zongyuan Ge


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Removing Adversarial Noise in Class Activation Feature Space


Apr 19, 2021
Dawei Zhou, Nannan Wang, Chunlei Peng, Xinbo Gao, Xiaoyu Wang, Jun Yu, Tongliang Liu


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Learning with Group Noise


Mar 17, 2021
Qizhou Wang, Jiangchao Yao, Chen Gong, Tongliang Liu, Mingming Gong, Hongxia Yang, Bo Han


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A Machine Learning Approach for Predicting Human Preference for Graph Layouts


Mar 01, 2021
Shijun Cai, Seok-Hee Hong, Jialiang Shen, Tongliang Liu

* 9 pages, 9 figures, PacificVis_Notes 

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Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation


Feb 28, 2021
Lie Ju, Xin Wang, Lin Wang, Dwarikanath Mahapatra, Xin Zhao, Mehrtash Harandi, Tom Drummond, Tongliang Liu, Zongyuan Ge


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Meta Discovery: Learning to Discover Novel Classes given Very Limited Data


Feb 18, 2021
Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Gang Niu, Bo Han


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Understanding the Interaction of Adversarial Training with Noisy Labels


Feb 09, 2021
Jianing Zhu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Hongxia Yang, Mohan Kankanhalli, Masashi Sugiyama


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Learning Diverse-Structured Networks for Adversarial Robustness


Feb 08, 2021
Xuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama

* 26 pages, 8 figures 

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Provably End-to-end Label-Noise Learning without Anchor Points


Feb 04, 2021
Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama


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Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model


Jan 14, 2021
Qizhou Wang, Bo Han, Tongliang Liu, Gang Niu, Jian Yang, Chen Gong


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