Learning with Feature-Dependent Label Noise: A Progressive Approach

• URL: http://arxiv.org/abs/2103.07756v2
• Date: Tue, 16 Mar 2021 07:28:12 GMT
• Title: Learning with Feature-Dependent Label Noise: A Progressive Approach
• Authors: Yikai Zhang, Songzhu Zheng, Pengxiang Wu, Mayank Goswami, Chao Chen
• Abstract summary: We propose a new family of feature-dependent label noise, which is much more general than commonly used i.i.d. label noise. 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.
• Score: 19.425199841491246
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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