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Simple and Effective Complementary Label Learning Based on Mean Square Error Loss
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Simple and Effective Complementary Label Learning Based on Mean Square Error Loss
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Journal Article
Simple and Effective Complementary Label Learning Based on Mean Square Error Loss
2023
Overview
Abstract In this paper, we propose a simple and effective complementary label learning approach to address the label noise problem for deep learning model. Different surrogate losses have been proposed for complementary label learning, however, are often sophisticated designed, as the losses are required to satisfy the classifier consistency property. We propose an effective square loss for complementary label learning under unbiased and biased assumptions. We also show theoretically that our method assurances that the optimal classifier under complementary labels is also the optimal classifier under ordinary labels. Finally, we test our method on three different benchmark datasets with biased and unbiased assumptions to verify the effectiveness of our method.
Publisher
IOP Publishing
Subject
