Related papers: Asymmetric Loss Functions for Learning with Noisy Labels

Asymmetric Loss Functions for Learning with Noisy Labels

URL: http://arxiv.org/abs/2106.03110v1
Date: Sun, 6 Jun 2021 12:52:48 GMT
Title: Asymmetric Loss Functions for Learning with Noisy Labels
Authors: Xiong Zhou, Xianming Liu, Junjun Jiang, Xin Gao, Xiangyang Ji
Abstract summary: We propose a new class of loss functions, namely textitasymmetric loss functions, which are robust to learning with noisy labels for various types of noise. Experimental results on benchmark datasets demonstrate that asymmetric loss functions can outperform state-of-the-art methods.
Score: 82.50250230688388
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Robust loss functions are essential for training deep neural networks with better generalization power in the presence of noisy labels. Symmetric loss functions are confirmed to be robust to label noise. However, the symmetric condition is overly restrictive. In this work, we propose a new class of loss functions, namely \textit{asymmetric loss functions}, which are robust to learning with noisy labels for various types of noise. We investigate general theoretical properties of asymmetric loss functions, including classification calibration, excess risk bound, and noise tolerance. Meanwhile, we introduce the asymmetry ratio to measure the asymmetry of a loss function. The empirical results show that a higher ratio would provide better noise tolerance. Moreover, we modify several commonly-used loss functions and establish the necessary and sufficient conditions for them to be asymmetric. Experimental results on benchmark datasets demonstrate that asymmetric loss functions can outperform state-of-the-art methods. The code is available at \href{https://github.com/hitcszx/ALFs}{https://github.com/hitcszx/ALFs}

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