Meta Label Correction for Noisy Label Learning

TL;DR

This paper introduces MLC, a meta-learning framework that performs label correction for noisy labels, improving over previous reweighting methods in image and text classification tasks.

Contribution

The paper proposes a novel meta-learning based label correction approach, extending prior reweighting methods to directly correct noisy labels within a bi-level optimization framework.

Findings

MLC outperforms previous reweighting methods in noisy label scenarios.

Label correction effectively reduces the impact of noisy labels.

The approach is validated on both image and text classification tasks.

Abstract

Leveraging weak or noisy supervision for building effective machine learning models has long been an important research problem. Its importance has further increased recently due to the growing need for large-scale datasets to train deep learning models. Weak or noisy supervision could originate from multiple sources including non-expert annotators or automatic labeling based on heuristics or user interaction signals. There is an extensive amount of previous work focusing on leveraging noisy labels. Most notably, recent work has shown impressive gains by using a meta-learned instance re-weighting approach where a meta-learning framework is used to assign instance weights to noisy labels. In this paper, we extend this approach via posing the problem as label correction problem within a meta-learning framework. We view the label correction procedure as a meta-process and propose a new meta-learning based framework termed MLC (Meta Label Correction) for learning with noisy labels. Specifically, a label correction network is adopted as a meta-model to produce corrected labels for noisy labels while the main model is trained to leverage the corrected labeled. Both models are jointly trained by solving a bi-level optimization problem. We run extensive experiments with different label noise levels and types on both image recognition and text classification tasks. We compare the reweighing and correction approaches showing that the correction framing addresses some of the limitation of reweighting. We also show that the proposed MLC approach achieves large improvements over previous methods in many settings.