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arXiv · 2103.07756

Learning with Feature-Dependent Label Noise: A Progressive Approach

Yikai Zhang·Songzhu Zheng·Pengxiang Wu·Mayank Goswami·Chao Chen
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Abstract

Label noise is frequently observed in real-world large-scale datasets. The noise is introduced due to a variety of reasons; it is heterogeneous and feature-dependent. Most existing approaches to handling noisy labels fall into two categories: they either assume an ideal feature-independent noise, or remain heuristic without theoretical guarantees. In this paper, we propose to target a new family of feature-dependent label noise, which is much more general than commonly used i.i.d. label noise and encompasses a broad spectrum of noise patterns. Focusing on this general noise family, we propose a progressive label correction algorithm that iteratively corrects labels and refines the model. We provide theoretical guarantees showing that for a wide variety of (unknown) noise patterns, a classifier trained with this strategy converges to be consistent with the Bayes classifier. In experiments, our method outperforms SOTA baselines and is robust to various noise types and levels.

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Yikai Zhang, Songzhu Zheng, Pengxiang Wu, Mayank Goswami, Chao Chen. 2021-03-13. Learning with Feature-Dependent Label Noise: A Progressive Approach. https://arxiv.org/abs/2103.07756

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