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2020 – today
- 2023
[j59]Jie Ma
, Jun Liu, Yaxian Wang, Junjun Li, Tongliang Liu:
Relation-Aware Fine-Grained Reasoning Network for Textbook Question Answering. IEEE Trans. Neural Networks Learn. Syst. 34(1): 15-27 (2023)- 2022
- [j58]Xu Yang, Cheng Deng, Tongliang Liu, Dacheng Tao:
Heterogeneous Graph Attention Network for Unsupervised Multiple-Target Domain Adaptation. IEEE Trans. Pattern Anal. Mach. Intell. 44(4): 1992-2003 (2022) - [j57]Chen Gong, Qizhou Wang, Tongliang Liu, Bo Han, Jane You, Jian Yang, Dacheng Tao:
Instance-Dependent Positive and Unlabeled Learning With Labeling Bias Estimation. IEEE Trans. Pattern Anal. Mach. Intell. 44(8): 4163-4177 (2022) - [j56]Hao Wang, Cheng Deng, Tongliang Liu, Dacheng Tao:
Transferable Coupled Network for Zero-Shot Sketch-Based Image Retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 44(12): 9181-9194 (2022) - [j55]Shuo Yang, Songhua Wu, Tongliang Liu, Min Xu:
Bridging the Gap Between Few-Shot and Many-Shot Learning via Distribution Calibration. IEEE Trans. Pattern Anal. Mach. Intell. 44(12): 9830-9843 (2022) - [j54]Guoqing Bao, Huai Chen, Tongliang Liu, Guanzhong Gong, Yong Yin, Lisheng Wang, Xiuying Wang:
COVID-MTL: Multitask learning with Shift3D and random-weighted loss for COVID-19 diagnosis and severity assessment. Pattern Recognit. 124: 108499 (2022) - [j53]Jingchen Ke, Chen Gong, Tongliang Liu, Lin Zhao, Jian Yang, Dacheng Tao:
Laplacian Welsch Regularization for Robust Semisupervised Learning. IEEE Trans. Cybern. 52(1): 164-177 (2022) - [j52]Xinpeng Ding, Nannan Wang, Shiwei Zhang, Ziyuan Huang, Xiaomeng Li, Mingqian Tang, Tongliang Liu, Xinbo Gao:
Exploring Language Hierarchy for Video Grounding. IEEE Trans. Image Process. 31: 4693-4706 (2022) - [j51]Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, Shan You, Dacheng Tao:
Quantum Differentially Private Sparse Regression Learning. IEEE Trans. Inf. Theory 68(8): 5217-5233 (2022) - [j50]Long Lan, Tongliang Liu, Xiang Zhang, Chuanfu Xu, Zhigang Luo:
Label Propagated Nonnegative Matrix Factorization for Clustering. IEEE Trans. Knowl. Data Eng. 34(1): 340-351 (2022) - [j49]Lie Ju, Xin Wang, Lin Wang, Dwarikanath Mahapatra, Xin Zhao, Quan Zhou, Tongliang Liu, Zongyuan Ge:
Improving Medical Images Classification With Label Noise Using Dual-Uncertainty Estimation. IEEE Trans. Medical Imaging 41(6): 1533-1546 (2022) - [j48]Zhaoyu Zhang, Mengyan Li, Haonian Xie, Jun Yu, Tongliang Liu, Chang Wen Chen:
TWGAN: Twin Discriminator Generative Adversarial Networks. IEEE Trans. Multim. 24: 677-688 (2022) - [j47]Zhengning Wu, Xiaobo Xia, Ruxin Wang, Jiatong Li, Jun Yu, Yinian Mao, Tongliang Liu:
LR-SVM+: Learning Using Privileged Information with Noisy Labels. IEEE Trans. Multim. 24: 1080-1092 (2022) - [j46]Jingwei Zhang, Tongliang Liu, Dacheng Tao:
On the Rates of Convergence From Surrogate Risk Minimizers to the Bayes Optimal Classifier. IEEE Trans. Neural Networks Learn. Syst. 33(10): 5766-5774 (2022) - [j45]Shijun Cai, Seok-Hee Hong, Xiaobo Xia, Tongliang Liu, Weidong Huang:
A machine learning approach for predicting human shortest path task performance. Vis. Informatics 6(2): 50-61 (2022) - [c89]Amirmohammad Pasdar, Young Choon Lee, Tongliang Liu, Seok-Hee Hong:
Train Me to Fight: Machine-Learning Based On-Device Malware Detection for Mobile Devices. CCGRID 2022: 239-248 - [c88]Masashi Sugiyama, Tongliang Liu, Bo Han, Yang Liu, Gang Niu:
Learning and Mining with Noisy Labels. CIKM 2022: 5152-5155 - [c87]Songhua Wu, Mingming Gong, Bo Han, Yang Liu, Tongliang Liu:
Fair Classification with Instance-dependent Label Noise. CLeaR 2022: 927-943 - [c86]Shikun Li, Xiaobo Xia, Shiming Ge, Tongliang Liu:
Selective-Supervised Contrastive Learning with Noisy Labels. CVPR 2022: 316-325 - [c85]Xiang An, Jiankang Deng, Jia Guo, Ziyong Feng, Xuhan Zhu, Jing Yang, Tongliang Liu:
Killing Two Birds with One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC. CVPR 2022: 4032-4041 - [c84]Xiaoqing Guo, Jie Liu, Tongliang Liu, Yixuan Yuan:
SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation. CVPR 2022: 7022-7031 - [c83]Erkun Yang, Dongren Yao, Tongliang Liu, Cheng Deng:
Mutual Quantization for Cross-Modal Search with Noisy Labels. CVPR 2022: 7541-7550 - [c82]Zhaoqing Wang, Yu Lu, Qiang Li, Xunqiang Tao, Yandong Guo, Mingming Gong, Tongliang Liu:
CRIS: CLIP-Driven Referring Image Segmentation. CVPR 2022: 11676-11685 - [c81]Zhaoqing Wang, Qiang Li, Guoxin Zhang, Pengfei Wan, Wen Zheng, Nannan Wang, Mingming Gong, Tongliang Liu:
Exploring Set Similarity for Dense Self-supervised Representation Learning. CVPR 2022: 16569-16578 - [c80]De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang, Bo Han, Gang Niu, Xinbo Gao, Masashi Sugiyama:
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation. CVPR 2022: 16609-16618 - [c79]Yongqiang Chen, Han Yang, Yonggang Zhang, Kaili Ma, Tongliang Liu, Bo Han, James Cheng:
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability. ICLR 2022 - [c78]Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, Gang Niu, Mingyuan Zhou, Masashi Sugiyama:
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data. ICLR 2022 - [c77]Jiaheng Wei, Zhaowei Zhu, Hao Cheng, Tongliang Liu, Gang Niu, Yang Liu:
Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations. ICLR 2022 - [c76]Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama:
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. ICLR 2022 - [c75]Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Gang Niu, Masashi Sugiyama, Dacheng Tao:
Rethinking Class-Prior Estimation for Positive-Unlabeled Learning. ICLR 2022 - [c74]Fei Zhang, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Tao Qin, Masashi Sugiyama:
Exploiting Class Activation Value for Partial-Label Learning. ICLR 2022 - [c73]Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, Kun Zhang:
Adversarial Robustness Through the Lens of Causality. ICLR 2022 - [c72]Jianing Zhu, Jiangchao Yao, Bo Han, Jingfeng Zhang, Tongliang Liu, Gang Niu, Jingren Zhou, Jianliang Xu, Hongxia Yang:
Reliable Adversarial Distillation with Unreliable Teachers. ICLR 2022 - [c71]Joshua Y. Kim, Tongliang Liu, Kalina Yacef:
Improving Supervised Learning in Conversational Analysis through Reusing Preprocessing Data as Auxiliary Supervisors. ICMI Companion 2022: 134-143 - [c70]Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Masashi Sugiyama, Yang Liu:
To Smooth or Not? When Label Smoothing Meets Noisy Labels. ICML 2022: 23589-23614 - [c69]Shuo Yang, Erkun Yang, Bo Han, Yang Liu, Min Xu, Gang Niu, Tongliang Liu:
Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network. ICML 2022: 25302-25312 - [c68]Chaojian Yu, Bo Han, Li Shen, Jun Yu, Chen Gong, Mingming Gong, Tongliang Liu:
Understanding Robust Overfitting of Adversarial Training and Beyond. ICML 2022: 25595-25610 - [c67]Dawei Zhou, Nannan Wang, Xinbo Gao, Bo Han, Xiaoyu Wang, Yibing Zhan, Tongliang Liu:
Improving Adversarial Robustness via Mutual Information Estimation. ICML 2022: 27338-27352 - [c66]Dawei Zhou, Nannan Wang, Bo Han, Tongliang Liu:
Modeling Adversarial Noise for Adversarial Training. ICML 2022: 27353-27366 - [c65]Chaojian Yu, Bo Han, Mingming Gong, Li Shen, Shiming Ge, Du Bo, Tongliang Liu:
Robust Weight Perturbation for Adversarial Training. IJCAI 2022: 3688-3694 - [c64]Xiong Peng, Feng Liu, Jingfeng Zhang, Long Lan, Junjie Ye, Tongliang Liu, Bo Han:
Bilateral Dependency Optimization: Defending Against Model-inversion Attacks. KDD 2022: 1358-1367 - [c63]Xiaobo Xia, Shuo Shan, Mingming Gong, Nannan Wang, Fei Gao, Haikun Wei, Tongliang Liu:
Sample-Efficient Kernel Mean Estimator with Marginalized Corrupted Data. KDD 2022: 2110-2119 - [c62]Xin Jin, Tianyu He, Xu Shen, Tongliang Liu, Xinchao Wang, Jianqiang Huang, Zhibo Chen, Xian-Sheng Hua:
Meta Clustering Learning for Large-scale Unsupervised Person Re-identification. ACM Multimedia 2022: 2163-2172 - [c61]Yong Luo, Ling-Yu Duan, Yan Bai, Tongliang Liu, Yihang Lou, Yonggang Wen:
Nonlinear Multi-Model Reuse. MMSP 2022: 1-6 - [c60]Amirmohammad Pasdar, Young Choon Lee, Seok-Hee Hong, Tongliang Liu:
MAPS: a dataset for semantic profiling and analysis of Android applications. MobiArch@MobiCom 2022: 13-18 - [i114]Yexiong Lin, Yu Yao, Yuxuan Du, Jun Yu, Bo Han, Mingming Gong, Tongliang Liu:
Do We Need to Penalize Variance of Losses for Learning with Label Noise? CoRR abs/2201.12739 (2022) - [i113]Yongqiang Chen, Yonggang Zhang, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, James Cheng:
Invariance Principle Meets Out-of-Distribution Generalization on Graphs. CoRR abs/2202.05441 (2022) - [i112]Yongqiang Chen, Han Yang, Yonggang Zhang, Kaili Ma, Tongliang Liu, Bo Han, James Cheng:
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability. CoRR abs/2202.08057 (2022) - [i111]Shikun Li, Xiaobo Xia, Shiming Ge, Tongliang Liu:
Selective-Supervised Contrastive Learning with Noisy Labels. CoRR abs/2203.04181 (2022) - [i110]Shikun Li, Tongliang Liu, Jiyong Tan, Dan Zeng, Shiming Ge:
Trustable Co-label Learning from Multiple Noisy Annotators. CoRR abs/2203.04199 (2022) - [i109]Xiaoqing Guo, Jie Liu, Tongliang Liu, Yixuan Yuan:
SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation. CoRR abs/2203.15202 (2022) - [i108]Xiang An, Jiankang Deng, Jia Guo, Ziyong Feng, Xuhan Zhu, Jing Yang, Tongliang Liu:
Killing Two Birds with One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC. CoRR abs/2203.15565 (2022) - [i107]Chuang Liu, Yibing Zhan, Chang Li, Bo Du, Jia Wu, Wenbin Hu, Tongliang Liu, Dacheng Tao:
Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities. CoRR abs/2204.07321 (2022) - [i106]Xiaobo Xia, Wenhao Yang, Jie Ren, Yewen Li, Yibing Zhan, Bo Han, Tongliang Liu:
Pluralistic Image Completion with Probabilistic Mixture-of-Experts. CoRR abs/2205.09086 (2022) - [i105]Erdun Gao, Ignavier Ng, Mingming Gong, Li Shen, Wei Huang, Tongliang Liu, Kun Zhang, Howard Bondell:
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models. CoRR abs/2205.13869 (2022) - [i104]Aoqi Zuo, Susan Wei, Tongliang Liu, Bo Han, Kun Zhang, Mingming Gong:
Counterfactual Fairness with Partially Known Causal Graph. CoRR abs/2205.13972 (2022) - [i103]Chaojian Yu, Bo Han, Mingming Gong, Li Shen, Shiming Ge, Bo Du, Tongliang Liu:
Robust Weight Perturbation for Adversarial Training. CoRR abs/2205.14826 (2022) - [i102]Yingbin Bai, Erkun Yang, Zhaoqing Wang, Yuxuan Du, Bo Han, Cheng Deng, Dadong Wang, Tongliang Liu:
MSR: Making Self-supervised learning Robust to Aggressive Augmentations. CoRR abs/2206.01999 (2022) - [i101]De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang, Bo Han, Gang Niu, Xinbo Gao, Masashi Sugiyama:
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation. CoRR abs/2206.02791 (2022) - [i100]Jinkai Tian, Xiaoyu Sun, Yuxuan Du, Shanshan Zhao, Qing Liu, Kaining Zhang, Wei Yi, Wanrong Huang, Chaoyue Wang, Xingyao Wu, Min-Hsiu Hsieh, Tongliang Liu, Wenjing Yang, Dacheng Tao:
Recent Advances for Quantum Neural Networks in Generative Learning. CoRR abs/2206.03066 (2022) - [i99]Xiong Peng, Feng Liu, Jingfen Zhang, Long Lan, Junjie Ye, Tongliang Liu, Bo Han:
Bilateral Dependency Optimization: Defending Against Model-inversion Attacks. CoRR abs/2206.05483 (2022) - [i98]Lianyang Ma, Yu Yao, Tao Liang, Tongliang Liu:
Multi-scale Cooperative Multimodal Transformers for Multimodal Sentiment Analysis in Videos. CoRR abs/2206.07981 (2022) - [i97]Chaojian Yu, Bo Han, Li Shen, Jun Yu, Chen Gong, Mingming Gong, Tongliang Liu:
Understanding Robust Overfitting of Adversarial Training and Beyond. CoRR abs/2206.08675 (2022) - [i96]Zhuo Huang, Xiaobo Xia, Li Shen, Bo Han, Mingming Gong, Chen Gong, Tongliang Liu:
Harnessing Out-Of-Distribution Examples via Augmenting Content and Style. CoRR abs/2207.03162 (2022) - [i95]Dawei Zhou, Nannan Wang, Xinbo Gao, Bo Han, Xiaoyu Wang, Yibing Zhan, Tongliang Liu:
Improving Adversarial Robustness via Mutual Information Estimation. CoRR abs/2207.12203 (2022) - [i94]Xinbiao Wang, Junyu Liu, Tongliang Liu, Yong Luo, Yuxuan Du, Dacheng Tao:
Symmetric Pruning in Quantum Neural Networks. CoRR abs/2208.14057 (2022) - [i93]Chenghao Sun, Yonggang Zhang, Chaoqun Wan, Qizhou Wang, Ya Li, Tongliang Liu, Bo Han, Xinmei Tian:
Towards Lightweight Black-Box Attacks against Deep Neural Networks. CoRR abs/2209.14826 (2022) - [i92]Chaojian Yu, Dawei Zhou, Li Shen, Jun Yu, Bo Han, Mingming Gong, Nannan Wang, Tongliang Liu:
Strength-Adaptive Adversarial Training. CoRR abs/2210.01288 (2022) - [i91]Yuanyuan Wang, Wei Huang, Mingming Gong, Xi Geng, Tongliang Liu, Kun Zhang, Dacheng Tao:
Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations. CoRR abs/2210.05955 (2022) - [i90]Qizhou Wang, Feng Liu, Yonggang Zhang, Jing Zhang, Chen Gong, Tongliang Liu, Bo Han:
Watermarking for Out-of-distribution Detection. CoRR abs/2210.15198 (2022) - [i89]Jianan Zhou, Jianing Zhu, Jingfeng Zhang, Tongliang Liu, Gang Niu, Bo Han, Masashi Sugiyama:
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks. CoRR abs/2211.00269 (2022) - [i88]Maoyuan Ye, Jing Zhang, Shanshan Zhao, Juhua Liu, Tongliang Liu, Bo Du, Dacheng Tao:
DeepSolo: Let Transformer Decoder with Explicit Points Solo for Text Spotting. CoRR abs/2211.10772 (2022) - [i87]Huaxi Huang, Hui Kang, Sheng Liu, Olivier Salvado, Thierry Rakotoarivelo, Dadong Wang, Tongliang Liu:
PADDLES: Phase-Amplitude Spectrum Disentangled Early Stopping for Learning with Noisy Labels. CoRR abs/2212.03462 (2022) - 2021
- [j44]Zhe Chen, Wanli Ouyang, Tongliang Liu, Dacheng Tao:
A Shape Transformation-based Dataset Augmentation Framework for Pedestrian Detection. Int. J. Comput. Vis. 129(4): 1121-1138 (2021) - [j43]Chen Gong, Hong Shi, Tongliang Liu, Chuang Zhang, Jian Yang, Dacheng Tao:
Loss Decomposition and Centroid Estimation for Positive and Unlabeled Learning. IEEE Trans. Pattern Anal. Mach. Intell. 43(3): 918-932 (2021) - [j42]Shuai Li, Kui Jia, Yuxin Wen, Tongliang Liu, Dacheng Tao:
Orthogonal Deep Neural Networks. IEEE Trans. Pattern Anal. Mach. Intell. 43(4): 1352-1368 (2021) - [j41]Jia Shao, Bo Du, Chen Wu, Mingming Gong, Tongliang Liu:
HRSiam: High-Resolution Siamese Network, Towards Space-Borne Satellite Video Tracking. IEEE Trans. Image Process. 30: 3056-3068 (2021) - [j40]Xinpeng Ding, Nannan Wang, Xinbo Gao, Jie Li, Xiaoyu Wang, Tongliang Liu:
KFC: An Efficient Framework for Semi-Supervised Temporal Action Localization. IEEE Trans. Image Process. 30: 6869-6878 (2021) - [c59]Qizhou Wang, Bo Han, Tongliang Liu, Gang Niu, Jian Yang, Chen Gong:
Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model. AAAI 2021: 10183-10191 - [c58]Qizhou Wang, Jiangchao Yao, Chen Gong, Tongliang Liu, Mingming Gong, Hongxia Yang, Bo Han:
Learning with Group Noise. AAAI 2021: 10192-10200 - [c57]Shijun Cai, Seok-Hee Hong, Jialiang Shen, Tongliang Liu:
A Machine Learning Approach for Predicting Human Preference for Graph Layouts*. PacificVis 2021: 6-10 - [c56]Zhen Huang, Xu Shen, Jun Xing, Tongliang Liu, Xinmei Tian, Houqiang Li, Bing Deng, Jianqiang Huang, Xian-Sheng Hua:
Revisiting Knowledge Distillation: An Inheritance and Exploration Framework. CVPR 2021: 3579-3588 - [c55]Zhaowei Zhu, Tongliang Liu, Yang Liu:
A Second-Order Approach to Learning With Instance-Dependent Label Noise. CVPR 2021: 10113-10123 - [c54]Dawei Zhou, Nannan Wang, Chunlei Peng, Xinbo Gao, Xiaoyu Wang, Jun Yu, Tongliang Liu:
Removing Adversarial Noise in Class Activation Feature Space. ICCV 2021: 7858-7867 - [c53]Yingbin Bai, Tongliang Liu:
Me-Momentum: Extracting Hard Confident Examples from Noisily Labeled Data. ICCV 2021: 9292-9301 - [c52]Jun Yu, Xinlong Hao, Zeyu Cui, Peng He, Tongliang Liu:
Boosting Fairness for Masked Face Recognition. ICCVW 2021: 1531-1540 - [c51]Xiaobo Xia, Tongliang Liu, Bo Han, Chen Gong, Nannan Wang, Zongyuan Ge, Yi Chang:
Robust early-learning: Hindering the memorization of noisy labels. ICLR 2021 - [c50]Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama:
Confidence Scores Make Instance-dependent Label-noise Learning Possible. ICML 2021: 825-836 - [c49]Xuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama:
Learning Diverse-Structured Networks for Adversarial Robustness. ICML 2021: 2880-2891 - [c48]Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama:
Maximum Mean Discrepancy Test is Aware of Adversarial Attacks. ICML 2021: 3564-3575 - [c47]Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama:
Provably End-to-end Label-noise Learning without Anchor Points. ICML 2021: 6403-6413 - [c46]Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu:
Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels. ICML 2021: 11285-11295 - [c45]Dawei Zhou, Tongliang Liu, Bo Han, Nannan Wang, Chunlei Peng, Xinbo Gao:
Towards Defending against Adversarial Examples via Attack-Invariant Features. ICML 2021: 12835-12845 - [c44]Zhaoqing Wang, Xiangyu Kong, Zhanbei Cui, Ming Wu, Chuang Zhang, Mingming Gong, Tongliang Liu:
Vecnet: A Spectral and Multi-Scale Spatial Fusion Deep Network for Pixel-Level Cloud Type Classification in Himawari-8 Imagery. IGARSS 2021: 4083-4086 - [c43]Lie Ju, Xin Wang, Lin Wang, Tongliang Liu, Xin Zhao, Tom Drummond, Dwarikanath Mahapatra, Zongyuan Ge:
Relational Subsets Knowledge Distillation for Long-Tailed Retinal Diseases Recognition. MICCAI (8) 2021: 3-12 - [c42]Jiahua Dong, Zhen Fang, Anjin Liu, Gan Sun, Tongliang Liu:
Confident Anchor-Induced Multi-Source Free Domain Adaptation. NeurIPS 2021: 2848-2860 - [c41]Yu Yao, Tongliang Liu, Mingming Gong, Bo Han, Gang Niu, Kun Zhang:
Instance-dependent Label-noise Learning under a Structural Causal Model. NeurIPS 2021: 4409-4420 - [c40]Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, William K. Cheung, James T. Kwok:
TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation. NeurIPS 2021: 20970-20982 - [c39]Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama:
Probabilistic Margins for Instance Reweighting in Adversarial Training. NeurIPS 2021: 23258-23269 - [c38]Yingbin Bai, Erkun Yang, Bo Han, Yanhua Yang, Jiatong Li, Yinian Mao, Gang Niu, Tongliang Liu:
Understanding and Improving Early Stopping for Learning with Noisy Labels. NeurIPS 2021: 24392-24403 - [c37]Jiayu He, Matloob Khushi, Nguyen Hoang Tran, Tongliang Liu:
Robust Dual Recurrent Neural Networks for Financial Time Series Prediction. SDM 2021: 747-755 - [i86]Qizhou Wang, Bo Han, Tongliang Liu, Gang Niu, Jian Yang, Chen Gong:
Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model. CoRR abs/2101.05467 (2021) - [i85]Xuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama:
Learning Diverse-Structured Networks for Adversarial Robustness. CoRR abs/2102.01886 (2021) - [i84]Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama:
Provably End-to-end Label-Noise Learning without Anchor Points. CoRR abs/2102.02400 (2021) - [i83]Jianing Zhu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Hongxia Yang, Mohan S. Kankanhalli, Masashi Sugiyama:
Understanding the Interaction of Adversarial Training with Noisy Labels. CoRR abs/2102.03482 (2021) - [i82]Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Gang Niu, Bo Han:
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data. CoRR abs/2102.04002 (2021) - [i81]Lie Ju, Xin Wang, Lin Wang, Dwarikanath Mahapatra, Xin Zhao, Mehrtash Harandi, Tom Drummond, Tongliang Liu, Zongyuan Ge:
Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation. CoRR abs/2103.00528 (2021) - [i80]Shijun Cai, Seok-Hee Hong, Jialiang Shen, Tongliang Liu:
A Machine Learning Approach for Predicting Human Preference for Graph Layouts. CoRR abs/2103.03665 (2021) - [i79]Qizhou Wang, Jiangchao Yao, Chen Gong, Tongliang Liu, Mingming Gong, Hongxia Yang, Bo Han:
Learning with Group Noise. CoRR abs/2103.09468 (2021) - [i78]Dawei Zhou, Nannan Wang, Chunlei Peng, Xinbo Gao, Xiaoyu Wang, Jun Yu, Tongliang Liu:
Removing Adversarial Noise in Class Activation Feature Space. CoRR abs/2104.09197 (2021) - [i77]Lie Ju, Xin Wang, Lin Wang, Tongliang Liu, Xin Zhao, Tom Drummond, Dwarikanath Mahapatra, Zongyuan Ge:
Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition. CoRR abs/2104.11057 (2021) - [i76]Shuo Yang, Erkun Yang, Bo Han, Yang Liu, Min Xu, Gang Niu, Tongliang Liu:
Estimating Instance-dependent Label-noise Transition Matrix using DNNs. CoRR abs/2105.13001 (2021) - [i75]Jingfeng Zhang, Xilie Xu, Bo Han, Tongliang Liu, Gang Niu, Lizhen Cui, Masashi Sugiyama:
NoiLIn: Do Noisy Labels Always Hurt Adversarial Training? CoRR abs/2105.14676 (2021) - [i74]Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama:
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. CoRR abs/2106.00445 (2021) - [i73]Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama:
Instance Correction for Learning with Open-set Noisy Labels. CoRR abs/2106.00455 (2021) - [i72]Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Yang Liu:
Understanding (Generalized) Label Smoothing when Learning with Noisy Labels. CoRR abs/2106.04149 (2021) - [i71]