Timezone: »
In contrast to the standard classification paradigm where the true class is given to each training pattern, complementary-label learning only uses training patterns each equipped with a complementary label, which only specifies one of the classes that the pattern does not belong to. The goal of this paper is to derive a novel framework of complementary-label learning with an unbiased estimator of the classification risk, for arbitrary losses and models---all existing methods have failed to achieve this goal. Not only is this beneficial for the learning stage, it also makes model/hyper-parameter selection (through cross-validation) possible without the need of any ordinarily labeled validation data, while using any linear/non-linear models or convex/non-convex loss functions. We further improve the risk estimator by a non-negative correction and gradient ascent trick, and demonstrate its superiority through experiments.
Author Information
Takashi Ishida (The University of Tokyo / RIKEN)
Gang Niu (RIKEN)

Gang Niu is currently an indefinite-term senior research scientist at RIKEN Center for Advanced Intelligence Project.
Aditya Menon (Australian National University)
Masashi Sugiyama (RIKEN / The University of Tokyo)
Related Events (a corresponding poster, oral, or spotlight)
-
2019 Oral: Complementary-Label Learning for Arbitrary Losses and Models »
Thu. Jun 13th 04:25 -- 04:30 PM Room Room 103
More from the Same Authors
-
2023 : Invited Talk 3: Masashi Sugiyama (RIKEN & UTokyo) - Data distribution shift »
Masashi Sugiyama -
2023 : Enriching Disentanglement: Definitions to Metrics »
Yivan Zhang · Masashi Sugiyama -
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: GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks »
Salah GHAMIZI · Jingfeng ZHANG · Maxime Cordy · Mike Papadakis · Masashi Sugiyama · YVES LE TRAON -
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: 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: Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits »
Jongyeong Lee · Junya Honda · Chao-Kai Chiang · Masashi Sugiyama -
2023 Poster: A Category-theoretical Meta-analysis of Definitions of Disentanglement »
Yivan Zhang · Masashi Sugiyama -
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: Adversarial Attack and Defense for Non-Parametric Two-Sample Tests »
Xilie Xu · Jingfeng Zhang · Feng Liu · Masashi Sugiyama · Mohan Kankanhalli -
2022 Poster: Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum »
Zeke Xie · Xinrui Wang · Huishuai Zhang · Issei Sato · Masashi Sugiyama -
2022 Spotlight: Adversarial Attack and Defense for Non-Parametric Two-Sample Tests »
Xilie Xu · Jingfeng Zhang · Feng Liu · Masashi Sugiyama · Mohan Kankanhalli -
2022 Oral: Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum »
Zeke Xie · Xinrui Wang · Huishuai Zhang · Issei Sato · Masashi Sugiyama -
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: 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: 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: Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences »
Ikko Yamane · Junya Honda · Florian YGER · Masashi Sugiyama -
2021 Poster: Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels »
Songhua Wu · Xiaobo Xia · Tongliang Liu · Bo Han · Mingming Gong · Nannan Wang · Haifeng Liu · Gang Niu -
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: Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences »
Ikko Yamane · Junya Honda · Florian YGER · 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 Spotlight: Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels »
Songhua Wu · Xiaobo Xia · Tongliang Liu · Bo Han · Mingming Gong · Nannan Wang · Haifeng Liu · Gang Niu -
2021 Oral: Confidence Scores Make Instance-dependent Label-noise Learning Possible »
Antonin Berthon · Bo Han · Gang Niu · Tongliang Liu · Masashi Sugiyama -
2021 Poster: Lower-Bounded Proper Losses for Weakly Supervised Classification »
Shuhei M Yoshida · Takashi Takenouchi · Masashi Sugiyama -
2021 Poster: Classification with Rejection Based on Cost-sensitive Classification »
Nontawat Charoenphakdee · Zhenghang Cui · Yivan Zhang · Masashi Sugiyama -
2021 Spotlight: Classification with Rejection Based on Cost-sensitive Classification »
Nontawat Charoenphakdee · Zhenghang Cui · Yivan Zhang · Masashi Sugiyama -
2021 Spotlight: Lower-Bounded Proper Losses for Weakly Supervised Classification »
Shuhei M Yoshida · Takashi Takenouchi · 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 Poster: Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization »
Zeke Xie · Li Yuan · Zhanxing Zhu · Masashi Sugiyama -
2021 Spotlight: Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization »
Zeke Xie · Li Yuan · Zhanxing Zhu · 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: Few-shot Domain Adaptation by Causal Mechanism Transfer »
Takeshi Teshima · Issei Sato · Masashi Sugiyama -
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: Online Dense Subgraph Discovery via Blurred-Graph Feedback »
Yuko Kuroki · Atsushi Miyauchi · Junya Honda · Masashi Sugiyama -
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: 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: Accelerating the diffusion-based ensemble sampling by non-reversible dynamics »
Futoshi Futami · Issei Sato · Masashi Sugiyama -
2020 Poster: Variational Imitation Learning with Diverse-quality Demonstrations »
Voot Tangkaratt · Bo Han · Mohammad Emtiyaz Khan · Masashi Sugiyama -
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 -
2020 Poster: Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian Analysis »
Yusuke Tsuzuku · Issei Sato · Masashi Sugiyama -
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 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 -
2019 Poster: Imitation Learning from Imperfect Demonstration »
Yueh-Hua Wu · Nontawat Charoenphakdee · Han Bao · Voot Tangkaratt · Masashi Sugiyama -
2019 Poster: On Symmetric Losses for Learning from Corrupted Labels »
Nontawat Charoenphakdee · Jongyeong Lee · Masashi Sugiyama -
2019 Oral: Imitation Learning from Imperfect Demonstration »
Yueh-Hua Wu · Nontawat Charoenphakdee · Han Bao · Voot Tangkaratt · Masashi Sugiyama -
2019 Oral: On Symmetric Losses for Learning from Corrupted Labels »
Nontawat Charoenphakdee · Jongyeong Lee · 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 -
2018 Poster: Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model »
Hideaki Imamura · Issei Sato · Masashi Sugiyama -
2018 Oral: Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model »
Hideaki Imamura · Issei Sato · Masashi Sugiyama -
2017 Poster: Learning Discrete Representations via Information Maximizing Self-Augmented Training »
Weihua Hu · Takeru Miyato · Seiya Tokui · Eiichi Matsumoto · Masashi Sugiyama -
2017 Talk: Learning Discrete Representations via Information Maximizing Self-Augmented Training »
Weihua Hu · Takeru Miyato · Seiya Tokui · Eiichi Matsumoto · 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