Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox
Picture for Tongliang Liu

Tongliang Liu

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

Figure 1 for Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition
Figure 2 for Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition
Figure 3 for Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition
Figure 4 for Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition

  Access Paper or Ask Questions

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

Figure 1 for Removing Adversarial Noise in Class Activation Feature Space
Figure 2 for Removing Adversarial Noise in Class Activation Feature Space
Figure 3 for Removing Adversarial Noise in Class Activation Feature Space
Figure 4 for Removing Adversarial Noise in Class Activation Feature Space

  Access Paper or Ask Questions

Learning with Group Noise


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

Figure 1 for Learning with Group Noise
Figure 2 for Learning with Group Noise
Figure 3 for Learning with Group Noise
Figure 4 for Learning with Group Noise

  Access Paper or Ask Questions

A Machine Learning Approach for Predicting Human Preference for Graph Layouts


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

Figure 1 for A Machine Learning Approach for Predicting Human Preference for Graph Layouts
Figure 2 for A Machine Learning Approach for Predicting Human Preference for Graph Layouts
Figure 3 for A Machine Learning Approach for Predicting Human Preference for Graph Layouts
Figure 4 for A Machine Learning Approach for Predicting Human Preference for Graph Layouts
* 9 pages, 9 figures, PacificVis_Notes 
  Access Paper or Ask Questions

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

Figure 1 for Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation
Figure 2 for Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation
Figure 3 for Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation
Figure 4 for Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation

  Access Paper or Ask Questions

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

Figure 1 for Meta Discovery: Learning to Discover Novel Classes given Very Limited Data
Figure 2 for Meta Discovery: Learning to Discover Novel Classes given Very Limited Data
Figure 3 for Meta Discovery: Learning to Discover Novel Classes given Very Limited Data
Figure 4 for Meta Discovery: Learning to Discover Novel Classes given Very Limited Data

  Access Paper or Ask Questions

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

Figure 1 for Understanding the Interaction of Adversarial Training with Noisy Labels
Figure 2 for Understanding the Interaction of Adversarial Training with Noisy Labels
Figure 3 for Understanding the Interaction of Adversarial Training with Noisy Labels
Figure 4 for Understanding the Interaction of Adversarial Training with Noisy Labels

  Access Paper or Ask Questions

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

Figure 1 for Learning Diverse-Structured Networks for Adversarial Robustness
Figure 2 for Learning Diverse-Structured Networks for Adversarial Robustness
Figure 3 for Learning Diverse-Structured Networks for Adversarial Robustness
Figure 4 for Learning Diverse-Structured Networks for Adversarial Robustness
* 26 pages, 8 figures 
  Access Paper or Ask Questions

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


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

Figure 1 for Provably End-to-end Label-Noise Learning without Anchor Points
Figure 2 for Provably End-to-end Label-Noise Learning without Anchor Points
Figure 3 for Provably End-to-end Label-Noise Learning without Anchor Points
Figure 4 for Provably End-to-end Label-Noise Learning without Anchor Points

  Access Paper or Ask Questions

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

Figure 1 for Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model
Figure 2 for Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model
Figure 3 for Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model
Figure 4 for Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model

  Access Paper or Ask Questions

COVID-MTL: Multitask Learning with Shift3D and Random-weighted Loss for Automated Diagnosis and Severity Assessment of COVID-19


Dec 31, 2020
Guoqing Bao, Huai Chen, Tongliang Liu, Guanzhong Gong, Yong Yin, Lisheng Wang, Xiuying Wang

Figure 1 for COVID-MTL: Multitask Learning with Shift3D and Random-weighted Loss for Automated Diagnosis and Severity Assessment of COVID-19
Figure 2 for COVID-MTL: Multitask Learning with Shift3D and Random-weighted Loss for Automated Diagnosis and Severity Assessment of COVID-19
Figure 3 for COVID-MTL: Multitask Learning with Shift3D and Random-weighted Loss for Automated Diagnosis and Severity Assessment of COVID-19
Figure 4 for COVID-MTL: Multitask Learning with Shift3D and Random-weighted Loss for Automated Diagnosis and Severity Assessment of COVID-19
* COVID-19 research; computer vision and pattern recognition; 13 pages, 10 figures and 5 tables 
  Access Paper or Ask Questions

A Second-Order Approach to Learning with Instance-Dependent Label Noise


Dec 22, 2020
Zhaowei Zhu, Tongliang Liu, Yang Liu

Figure 1 for A Second-Order Approach to Learning with Instance-Dependent Label Noise
Figure 2 for A Second-Order Approach to Learning with Instance-Dependent Label Noise
Figure 3 for A Second-Order Approach to Learning with Instance-Dependent Label Noise
Figure 4 for A Second-Order Approach to Learning with Instance-Dependent Label Noise
* Learning with label noise 
  Access Paper or Ask Questions

Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels


Dec 02, 2020
Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Jiankang Deng, Jiatong Li, Yinian Mao

Figure 1 for Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels
Figure 2 for Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels
Figure 3 for Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels
Figure 4 for Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels

  Access Paper or Ask Questions

A Survey of Label-noise Representation Learning: Past, Present and Future


Nov 09, 2020
Bo Han, Quanming Yao, Tongliang Liu, Gang Niu, Ivor W. Tsang, James T. Kwok, Masashi Sugiyama

Figure 1 for A Survey of Label-noise Representation Learning: Past, Present and Future
Figure 2 for A Survey of Label-noise Representation Learning: Past, Present and Future
Figure 3 for A Survey of Label-noise Representation Learning: Past, Present and Future
Figure 4 for A Survey of Label-noise Representation Learning: Past, Present and Future
* The draft is kept updating; any comments and suggestions are welcome 
  Access Paper or Ask Questions

Maximum Mean Discrepancy is Aware of Adversarial Attacks


Oct 22, 2020
Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama

Figure 1 for Maximum Mean Discrepancy is Aware of Adversarial Attacks
Figure 2 for Maximum Mean Discrepancy is Aware of Adversarial Attacks
Figure 3 for Maximum Mean Discrepancy is Aware of Adversarial Attacks
Figure 4 for Maximum Mean Discrepancy is Aware of Adversarial Attacks

  Access Paper or Ask Questions

Experimental Quantum Generative Adversarial Networks for Image Generation


Oct 21, 2020
He-Liang Huang, Yuxuan Du, Ming Gong, Youwei Zhao, Yulin Wu, Chaoyue Wang, Shaowei Li, Futian Liang, Jin Lin, Yu Xu, Rui Yang, Tongliang Liu, Min-Hsiu Hsieh, Hui Deng, Hao Rong, Cheng-Zhi Peng, Chao-Yang Lu, Yu-Ao Chen, Dacheng Tao, Xiaobo Zhu, Jian-Wei Pan

Figure 1 for Experimental Quantum Generative Adversarial Networks for Image Generation
Figure 2 for Experimental Quantum Generative Adversarial Networks for Image Generation
Figure 3 for Experimental Quantum Generative Adversarial Networks for Image Generation
Figure 4 for Experimental Quantum Generative Adversarial Networks for Image Generation
* Our first version was submitted to the journal in January 2020. Comments are welcome 
  Access Paper or Ask Questions

Quantum differentially private sparse regression learning


Jul 23, 2020
Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, Shan You, Dacheng Tao

Figure 1 for Quantum differentially private sparse regression learning
Figure 2 for Quantum differentially private sparse regression learning
Figure 3 for Quantum differentially private sparse regression learning

  Access Paper or Ask Questions

Weakly Supervised Temporal Action Localization with Segment-Level Labels


Jul 03, 2020
Xinpeng Ding, Nannan Wang, Xinbo Gao, Jie Li, Xiaoyu Wang, Tongliang Liu

Figure 1 for Weakly Supervised Temporal Action Localization with Segment-Level Labels
Figure 2 for Weakly Supervised Temporal Action Localization with Segment-Level Labels
Figure 3 for Weakly Supervised Temporal Action Localization with Segment-Level Labels
Figure 4 for Weakly Supervised Temporal Action Localization with Segment-Level Labels
* 18 pages,7 figures 
  Access Paper or Ask Questions

Parts-dependent Label Noise: Towards Instance-dependent Label Noise


Jun 14, 2020
Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, Masashi Sugiyama

Figure 1 for Parts-dependent Label Noise: Towards Instance-dependent Label Noise
Figure 2 for Parts-dependent Label Noise: Towards Instance-dependent Label Noise
Figure 3 for Parts-dependent Label Noise: Towards Instance-dependent Label Noise
Figure 4 for Parts-dependent Label Noise: Towards Instance-dependent Label Noise

  Access Paper or Ask Questions

Class2Simi: A New Perspective on Learning with Label Noise


Jun 14, 2020
Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu

Figure 1 for Class2Simi: A New Perspective on Learning with Label Noise
Figure 2 for Class2Simi: A New Perspective on Learning with Label Noise
Figure 3 for Class2Simi: A New Perspective on Learning with Label Noise
Figure 4 for Class2Simi: A New Perspective on Learning with Label Noise

  Access Paper or Ask Questions

Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning


Jun 14, 2020
Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Jiankang Deng, Gang Niu, Masashi Sugiyama

Figure 1 for Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning
Figure 2 for Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning
Figure 3 for Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning
Figure 4 for Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning

  Access Paper or Ask Questions

Repulsive Mixture Models of Exponential Family PCA for Clustering


Apr 07, 2020
Maoying Qiao, Tongliang Liu, Jun Yu, Wei Bian, Dacheng Tao

Figure 1 for Repulsive Mixture Models of Exponential Family PCA for Clustering
Figure 2 for Repulsive Mixture Models of Exponential Family PCA for Clustering
Figure 3 for Repulsive Mixture Models of Exponential Family PCA for Clustering
Figure 4 for Repulsive Mixture Models of Exponential Family PCA for Clustering

  Access Paper or Ask Questions

Quantum noise protects quantum classifiers against adversaries


Mar 20, 2020
Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, Dacheng Tao, Nana Liu

Figure 1 for Quantum noise protects quantum classifiers against adversaries
Figure 2 for Quantum noise protects quantum classifiers against adversaries
Figure 3 for Quantum noise protects quantum classifiers against adversaries
Figure 4 for Quantum noise protects quantum classifiers against adversaries
* 16 pages, 8 figures 
  Access Paper or Ask Questions

Multi-Class Classification from Noisy-Similarity-Labeled Data


Feb 16, 2020
Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu

Figure 1 for Multi-Class Classification from Noisy-Similarity-Labeled Data
Figure 2 for Multi-Class Classification from Noisy-Similarity-Labeled Data
Figure 3 for Multi-Class Classification from Noisy-Similarity-Labeled Data
Figure 4 for Multi-Class Classification from Noisy-Similarity-Labeled Data

  Access Paper or Ask Questions

Towards Mixture Proportion Estimation without Irreducibility


Feb 10, 2020
Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Gang Niu, Masashi Sugiyama, Dacheng Tao

Figure 1 for Towards Mixture Proportion Estimation without Irreducibility
Figure 2 for Towards Mixture Proportion Estimation without Irreducibility
Figure 3 for Towards Mixture Proportion Estimation without Irreducibility
Figure 4 for Towards Mixture Proportion Estimation without Irreducibility

  Access Paper or Ask Questions

Confidence Scores Make Instance-dependent Label-noise Learning Possible


Jan 11, 2020
Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama

Figure 1 for Confidence Scores Make Instance-dependent Label-noise Learning Possible
Figure 2 for Confidence Scores Make Instance-dependent Label-noise Learning Possible
Figure 3 for Confidence Scores Make Instance-dependent Label-noise Learning Possible
Figure 4 for Confidence Scores Make Instance-dependent Label-noise Learning Possible

  Access Paper or Ask Questions

A Shape Transformation-based Dataset Augmentation Framework for Pedestrian Detection


Dec 15, 2019
Zhe Chen, Wanli Ouyang, Tongliang Liu, Dacheng Tao

Figure 1 for A Shape Transformation-based Dataset Augmentation Framework for Pedestrian Detection
Figure 2 for A Shape Transformation-based Dataset Augmentation Framework for Pedestrian Detection
Figure 3 for A Shape Transformation-based Dataset Augmentation Framework for Pedestrian Detection
Figure 4 for A Shape Transformation-based Dataset Augmentation Framework for Pedestrian Detection

  Access Paper or Ask Questions