Estimating Noisy Class Posterior with Part-level Labels for Noisy Label Learning

• URL: http://arxiv.org/abs/2405.05714v2
• Date: Tue, 2 Jul 2024 07:06:15 GMT
• Title: Estimating Noisy Class Posterior with Part-level Labels for Noisy Label Learning
• Authors: Rui Zhao, Bin Shi, Jianfei Ruan, Tianze Pan, Bo Dong,
• Abstract summary: Existing methods typically learn noisy class posteriors by training a classification model with noisy labels. This paper proposes to augment the supervised information with part-level labels, encouraging the model to focus on and integrate richer information from various parts. Our method is theoretically sound, while experiments show that it is empirically effective in synthetic and real-world noisy benchmarks.
• Score: 13.502549812291878
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In noisy label learning, estimating noisy class posteriors plays a fundamental role for developing consistent classifiers, as it forms the basis for estimating clean class posteriors and the transition matrix. Existing methods typically learn noisy class posteriors by training a classification model with noisy labels. However, when labels are incorrect, these models may be misled to overemphasize the feature parts that do not reflect the instance characteristics, resulting in significant errors in estimating noisy class posteriors. To address this issue, this paper proposes to augment the supervised information with part-level labels, encouraging the model to focus on and integrate richer information from various parts. Specifically, our method first partitions features into distinct parts by cropping instances, yielding part-level labels associated with these various parts. Subsequently, we introduce a novel single-to-multiple transition matrix to model the relationship between the noisy and part-level labels, which incorporates part-level labels into a classifier-consistent framework. Utilizing this framework with part-level labels, we can learn the noisy class posteriors more precisely by guiding the model to integrate information from various parts, ultimately improving the classification performance. Our method is theoretically sound, while experiments show that it is empirically effective in synthetic and real-world noisy benchmarks.

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