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Oral
Confidence Scores Make Instance-dependent Label-noise Learning Possible
Antonin Berthon · Bo Han · Gang Niu · Tongliang Liu · Masashi Sugiyama

Thu Jul 22 05:00 AM -- 05:20 AM (PDT) @ None

In learning with noisy labels, for every instance, its label can randomly walk to other classes following a transition distribution which is named a noise model. Well-studied noise models are all instance-independent, namely, the transition depends only on the original label but not the instance itself, and thus they are less practical in the wild. Fortunately, methods based on instance-dependent noise have been studied, but most of them have to rely on strong assumptions on the noise models. To alleviate this issue, we introduce confidence-scored instance-dependent noise (CSIDN), where each instance-label pair is equipped with a confidence score. We find that with the help of confidence scores, the transition distribution of each instance can be approximately estimated. Similarly to the powerful forward correction for instance-independent noise, we propose a novel instance-level forward correction for CSIDN. We demonstrate the utility and effectiveness of our method through multiple experiments on datasets with synthetic label noise and real-world unknown noise.

Author Information

Antonin Berthon (BIOS Health)
Bo Han (HKBU / RIKEN)
Gang Niu (RIKEN)

Gang Niu is currently a research scientist (indefinite-term) at RIKEN Center for Advanced Intelligence Project. He received the PhD degree in computer science from Tokyo Institute of Technology in 2013. Before joining RIKEN as a research scientist, he was a senior software engineer at Baidu and then an assistant professor at the University of Tokyo. He has published more than 70 journal articles and conference papers, including 14 NeurIPS (1 oral and 3 spotlights), 28 ICML, and 2 ICLR (1 oral) papers. He has served as an area chair 14 times, including ICML 2019--2021, NeurIPS 2019--2021, and ICLR 2021--2022.

Tongliang Liu (The University of Sydney)
Masashi Sugiyama (RIKEN / The University of Tokyo)

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