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

Thu Jul 22 09:00 AM -- 11:00 AM (PDT) @
in Poster Session 5 »

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.

Keywords: Semi-supervised learning · Algorithms

Author Information

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

Gang Niu is currently an indefinite-term senior research scientist at RIKEN Center for Advanced Intelligence Project.

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

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