Confidence Scores Make Instance-dependent Label-noise Learning Possible

• URL: http://arxiv.org/abs/2001.03772v2
• Date: Mon, 22 Feb 2021 23:40:07 GMT
• Title: Confidence Scores Make Instance-dependent Label-noise Learning Possible
• Authors: Antonin Berthon and Bo Han and Gang Niu and Tongliang Liu and Masashi Sugiyama
• Abstract summary: 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. We introduce confidence-scored instance-dependent noise (CSIDN), where each instance-label pair is equipped with a confidence score. We find with the help of confidence scores, the transition distribution of each instance can be approximately estimated.
• Score: 129.84497190791103
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 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 under synthetic label noise and real-world unknown noise.

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