Timezone: »
Learning with noisy labels has attracted a lot of attention in recent years, where the mainstream approaches are in \emph{pointwise} manners. Meanwhile, \emph{pairwise} manners have shown great potential in supervised metric learning and unsupervised contrastive learning. Thus, a natural question is raised: does learning in a pairwise manner \emph{mitigate} label noise? To give an affirmative answer, in this paper, we propose a framework called \emph{Class2Simi}: it transforms data points with noisy \emph{class labels} to data pairs with noisy \emph{similarity labels}, where a similarity label denotes whether a pair shares the class label or not. Through this transformation, the \emph{reduction of the noise rate} is theoretically guaranteed, and hence it is in principle easier to handle noisy similarity labels. Amazingly, DNNs that predict the \emph{clean} class labels can be trained from noisy data pairs if they are first pretrained from noisy data points. Class2Simi is \emph{computationally efficient} because not only this transformation is on-the-fly in mini-batches, but also it just changes loss computation on top of model prediction into a pairwise manner. Its effectiveness is verified by extensive experiments.
Author Information
Songhua Wu (The University of Sydney)
Xiaobo Xia (The University of Sydney)
Tongliang Liu (The University of Sydney)
Bo Han (HKBU / RIKEN)
Mingming Gong (University of Melbourne)
Nannan Wang (Xidian University)
Haifeng Liu (Brain-Inspired Technology Co., Ltd.)
Gang Niu (RIKEN)

Gang Niu is currently an indefinite-term senior research scientist at RIKEN Center for Advanced Intelligence Project.
Related Events (a corresponding poster, oral, or spotlight)
-
2021 Poster: Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels »
Thu. Jul 22nd 04:00 -- 06:00 PM Room
More from the Same Authors
-
2022 : Invariance Principle Meets Out-of-Distribution Generalization on Graphs »
Yongqiang Chen · Yonggang Zhang · Yatao Bian · Han Yang · Kaili MA · Binghui Xie · Tongliang Liu · Bo Han · James Cheng -
2023 : Advancing Counterfactual Inference through Quantile Regression »
Shaoan Xie · Biwei Huang · Bin Gu · Tongliang Liu · Kun Zhang -
2023 Poster: Mitigating Memorization of Noisy Labels by Clipping the Model Prediction »
Hongxin Wei · HUIPING ZHUANG · RENCHUNZI XIE · Lei Feng · Gang Niu · Bo An · Sharon Li -
2023 Poster: Eliminating Adversarial Noise via Information Discard and Robust Representation Restoration »
Dawei Zhou · Yukun Chen · Nannan Wang · Decheng Liu · Xinbo Gao · Tongliang Liu -
2023 Poster: Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation »
Ruijiang Dong · Feng Liu · Haoang Chi · Tongliang Liu · Mingming Gong · Gang Niu · Masashi Sugiyama · Bo Han -
2023 Poster: Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability »
Jianing Zhu · Hengzhuang Li · Jiangchao Yao · Tongliang Liu · Jianliang Xu · Bo Han -
2023 Poster: A Universal Unbiased Method for Classification from Aggregate Observations »
Zixi Wei · Lei Feng · Bo Han · Tongliang Liu · Gang Niu · Xiaofeng Zhu · Heng Tao Shen -
2023 Poster: Exploring Model Dynamics for Accumulative Poisoning Discovery »
Jianing Zhu · Xiawei Guo · Jiangchao Yao · Chao Du · LI He · Shuo Yuan · Tongliang Liu · Liang Wang · Bo Han -
2023 Poster: Evolving Semantic Prototype Improves Generative Zero-Shot Learning »
Shiming Chen · Wenjin Hou · Ziming Hong · Xiaohan Ding · Yibing Song · Xinge You · Tongliang Liu · Kun Zhang -
2023 Poster: Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise? »
Yu Yao · Mingming Gong · Yuxuan Du · Jun Yu · Bo Han · Kun Zhang · Tongliang Liu -
2023 Poster: Phase-aware Adversarial Defense for Improving Adversarial Robustness »
Dawei Zhou · Nannan Wang · Heng Yang · Xinbo Gao · Tongliang Liu -
2023 Poster: Detecting Out-of-distribution Data through In-distribution Class Prior »
Xue JIANG · Feng Liu · zhen fang · Hong Chen · Tongliang Liu · Feng Zheng · Bo Han -
2022 Poster: Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network »
Shuo Yang · Erkun Yang · Bo Han · Yang Liu · Min Xu · Gang Niu · Tongliang Liu -
2022 Spotlight: Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network »
Shuo Yang · Erkun Yang · Bo Han · Yang Liu · Min Xu · Gang Niu · Tongliang Liu -
2022 Poster: Understanding Robust Overfitting of Adversarial Training and Beyond »
Chaojian Yu · Bo Han · Li Shen · Jun Yu · Chen Gong · Mingming Gong · Tongliang Liu -
2022 Poster: Modeling Adversarial Noise for Adversarial Training »
Dawei Zhou · Nannan Wang · Bo Han · Tongliang Liu -
2022 Poster: Improving Adversarial Robustness via Mutual Information Estimation »
Dawei Zhou · Nannan Wang · Xinbo Gao · Bo Han · Xiaoyu Wang · Yibing Zhan · Tongliang Liu -
2022 Spotlight: Understanding Robust Overfitting of Adversarial Training and Beyond »
Chaojian Yu · Bo Han · Li Shen · Jun Yu · Chen Gong · Mingming Gong · Tongliang Liu -
2022 Spotlight: Improving Adversarial Robustness via Mutual Information Estimation »
Dawei Zhou · Nannan Wang · Xinbo Gao · Bo Han · Xiaoyu Wang · Yibing Zhan · Tongliang Liu -
2022 Spotlight: Modeling Adversarial Noise for Adversarial Training »
Dawei Zhou · Nannan Wang · Bo Han · Tongliang Liu -
2022 Poster: Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack »
Ruize Gao · Jiongxiao Wang · Kaiwen Zhou · Feng Liu · Binghui Xie · Gang Niu · Bo Han · James Cheng -
2022 Poster: To Smooth or Not? When Label Smoothing Meets Noisy Labels »
Jiaheng Wei · Hangyu Liu · Tongliang Liu · Gang Niu · Masashi Sugiyama · Yang Liu -
2022 Spotlight: Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack »
Ruize Gao · Jiongxiao Wang · Kaiwen Zhou · Feng Liu · Binghui Xie · Gang Niu · Bo Han · James Cheng -
2022 Oral: To Smooth or Not? When Label Smoothing Meets Noisy Labels »
Jiaheng Wei · Hangyu Liu · Tongliang Liu · Gang Niu · Masashi Sugiyama · Yang Liu -
2021 Poster: Towards Defending against Adversarial Examples via Attack-Invariant Features »
Dawei Zhou · Tongliang Liu · Bo Han · Nannan Wang · Chunlei Peng · Xinbo Gao -
2021 Poster: Provably End-to-end Label-noise Learning without Anchor Points »
Xuefeng Li · Tongliang Liu · Bo Han · Gang Niu · Masashi Sugiyama -
2021 Poster: Learning Diverse-Structured Networks for Adversarial Robustness »
Xuefeng Du · Jingfeng Zhang · Bo Han · Tongliang Liu · Yu Rong · Gang Niu · Junzhou Huang · Masashi Sugiyama -
2021 Poster: CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection »
Hanshu YAN · Jingfeng Zhang · Gang Niu · Jiashi Feng · Vincent Tan · Masashi Sugiyama -
2021 Poster: Maximum Mean Discrepancy Test is Aware of Adversarial Attacks »
Ruize Gao · Feng Liu · Jingfeng Zhang · Bo Han · Tongliang Liu · Gang Niu · Masashi Sugiyama -
2021 Spotlight: CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection »
Hanshu YAN · Jingfeng Zhang · Gang Niu · Jiashi Feng · Vincent Tan · Masashi Sugiyama -
2021 Spotlight: Towards Defending against Adversarial Examples via Attack-Invariant Features »
Dawei Zhou · Tongliang Liu · Bo Han · Nannan Wang · Chunlei Peng · Xinbo Gao -
2021 Spotlight: Provably End-to-end Label-noise Learning without Anchor Points »
Xuefeng Li · Tongliang Liu · Bo Han · Gang Niu · Masashi Sugiyama -
2021 Spotlight: Learning Diverse-Structured Networks for Adversarial Robustness »
Xuefeng Du · Jingfeng Zhang · Bo Han · Tongliang Liu · Yu Rong · Gang Niu · Junzhou Huang · Masashi Sugiyama -
2021 Spotlight: Maximum Mean Discrepancy Test is Aware of Adversarial Attacks »
Ruize Gao · Feng Liu · Jingfeng Zhang · Bo Han · Tongliang Liu · Gang Niu · Masashi Sugiyama -
2021 Poster: Pointwise Binary Classification with Pairwise Confidence Comparisons »
Lei Feng · Senlin Shu · Nan Lu · Bo Han · Miao Xu · Gang Niu · Bo An · Masashi Sugiyama -
2021 Poster: Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification »
Nan Lu · Shida Lei · Gang Niu · Issei Sato · Masashi Sugiyama -
2021 Poster: Learning from Similarity-Confidence Data »
Yuzhou Cao · Lei Feng · Yitian Xu · Bo An · Gang Niu · Masashi Sugiyama -
2021 Poster: Confidence Scores Make Instance-dependent Label-noise Learning Possible »
Antonin Berthon · Bo Han · Gang Niu · Tongliang Liu · Masashi Sugiyama -
2021 Poster: Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization »
Yivan Zhang · Gang Niu · Masashi Sugiyama -
2021 Spotlight: Learning from Similarity-Confidence Data »
Yuzhou Cao · Lei Feng · Yitian Xu · Bo An · Gang Niu · Masashi Sugiyama -
2021 Spotlight: Pointwise Binary Classification with Pairwise Confidence Comparisons »
Lei Feng · Senlin Shu · Nan Lu · Bo Han · Miao Xu · Gang Niu · Bo An · Masashi Sugiyama -
2021 Spotlight: Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification »
Nan Lu · Shida Lei · Gang Niu · Issei Sato · Masashi Sugiyama -
2021 Oral: Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization »
Yivan Zhang · Gang Niu · Masashi Sugiyama -
2021 Oral: Confidence Scores Make Instance-dependent Label-noise Learning Possible »
Antonin Berthon · Bo Han · Gang Niu · Tongliang Liu · Masashi Sugiyama -
2021 Poster: Large-Margin Contrastive Learning with Distance Polarization Regularizer »
Shuo Chen · Gang Niu · Chen Gong · Jun Li · Jian Yang · Masashi Sugiyama -
2021 Spotlight: Large-Margin Contrastive Learning with Distance Polarization Regularizer »
Shuo Chen · Gang Niu · Chen Gong · Jun Li · Jian Yang · Masashi Sugiyama -
2020 Poster: Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks »
Yonggang Zhang · Ya Li · Tongliang Liu · Xinmei Tian -
2020 Poster: Do We Need Zero Training Loss After Achieving Zero Training Error? »
Takashi Ishida · Ikko Yamane · Tomoya Sakai · Gang Niu · Masashi Sugiyama -
2020 Poster: Progressive Identification of True Labels for Partial-Label Learning »
Jiaqi Lv · Miao Xu · LEI FENG · Gang Niu · Xin Geng · Masashi Sugiyama -
2020 Poster: Learning with Bounded Instance- and Label-dependent Label Noise »
Jiacheng Cheng · Tongliang Liu · Kotagiri Ramamohanarao · Dacheng Tao -
2020 Poster: SIGUA: Forgetting May Make Learning with Noisy Labels More Robust »
Bo Han · Gang Niu · Xingrui Yu · QUANMING YAO · Miao Xu · Ivor Tsang · Masashi Sugiyama -
2020 Poster: Label-Noise Robust Domain Adaptation »
Xiyu Yu · Tongliang Liu · Mingming Gong · Kun Zhang · Kayhan Batmanghelich · Dacheng Tao -
2020 Poster: Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels »
Yu-Ting Chou · Gang Niu · Hsuan-Tien (Tien) Lin · Masashi Sugiyama -
2020 Poster: Attacks Which Do Not Kill Training Make Adversarial Learning Stronger »
Jingfeng Zhang · Xilie Xu · Bo Han · Gang Niu · Lizhen Cui · Masashi Sugiyama · Mohan Kankanhalli -
2020 Poster: Variational Imitation Learning with Diverse-quality Demonstrations »
Voot Tangkaratt · Bo Han · Mohammad Emtiyaz Khan · Masashi Sugiyama -
2020 Poster: LTF: A Label Transformation Framework for Correcting Label Shift »
Jiaxian Guo · Mingming Gong · Tongliang Liu · Kun Zhang · Dacheng Tao -
2020 Poster: Learning with Multiple Complementary Labels »
LEI FENG · Takuo Kaneko · Bo Han · Gang Niu · Bo An · Masashi Sugiyama -
2020 Poster: Searching to Exploit Memorization Effect in Learning with Noisy Labels »
QUANMING YAO · Hansi Yang · Bo Han · Gang Niu · James Kwok -
2019 : Spotlight »
Tyler Scott · Kiran Thekumparampil · Jonathan Aigrain · Rene Bidart · Priyadarshini Panda · Dian Ang Yap · Yaniv Yacoby · Raphael Gontijo Lopes · Alberto Marchisio · Erik Englesson · Wanqian Yang · Moritz Graule · Yi Sun · Daniel Kang · Mike Dusenberry · Min Du · Hartmut Maennel · Kunal Menda · Vineet Edupuganti · Luke Metz · David Stutz · Vignesh Srinivasan · Timo Sämann · Vineeth N Balasubramanian · Sina Mohseni · Rob Cornish · Judith Butepage · Zhangyang Wang · Bai Li · Bo Han · Honglin Li · Maksym Andriushchenko · Lukas Ruff · Meet P. Vadera · Yaniv Ovadia · Sunil Thulasidasan · Disi Ji · Gang Niu · Saeed Mahloujifar · Aviral Kumar · SANGHYUK CHUN · Dong Yin · Joyce Xu Xu · Hugo Gomes · Raanan Rohekar -
2019 Poster: Classification from Positive, Unlabeled and Biased Negative Data »
Yu-Guan Hsieh · Gang Niu · Masashi Sugiyama -
2019 Poster: Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations »
Quanming Yao · James Kwok · Bo Han -
2019 Poster: Complementary-Label Learning for Arbitrary Losses and Models »
Takashi Ishida · Gang Niu · Aditya Menon · Masashi Sugiyama -
2019 Oral: Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations »
Quanming Yao · James Kwok · Bo Han -
2019 Oral: Complementary-Label Learning for Arbitrary Losses and Models »
Takashi Ishida · Gang Niu · Aditya Menon · Masashi Sugiyama -
2019 Oral: Classification from Positive, Unlabeled and Biased Negative Data »
Yu-Guan Hsieh · Gang Niu · Masashi Sugiyama -
2019 Poster: How does Disagreement Help Generalization against Label Corruption? »
Xingrui Yu · Bo Han · Jiangchao Yao · Gang Niu · Ivor Tsang · Masashi Sugiyama -
2019 Oral: How does Disagreement Help Generalization against Label Corruption? »
Xingrui Yu · Bo Han · Jiangchao Yao · Gang Niu · Ivor Tsang · Masashi Sugiyama -
2018 Poster: Classification from Pairwise Similarity and Unlabeled Data »
Han Bao · Gang Niu · Masashi Sugiyama -
2018 Oral: Classification from Pairwise Similarity and Unlabeled Data »
Han Bao · Gang Niu · Masashi Sugiyama -
2018 Poster: Does Distributionally Robust Supervised Learning Give Robust Classifiers? »
Weihua Hu · Gang Niu · Issei Sato · Masashi Sugiyama -
2018 Oral: Does Distributionally Robust Supervised Learning Give Robust Classifiers? »
Weihua Hu · Gang Niu · Issei Sato · Masashi Sugiyama -
2017 Poster: Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data »
Tomoya Sakai · Marthinus C du Plessis · Gang Niu · Masashi Sugiyama -
2017 Talk: Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data »
Tomoya Sakai · Marthinus C du Plessis · Gang Niu · Masashi Sugiyama