Combating Label Noise in Deep Learning Using Abstention

TL;DR

This paper presents a deep abstaining classifier (DAC) that improves robustness to label noise in deep learning by allowing the model to abstain on confusing samples, thereby enhancing learning and cleaning noisy data.

Contribution

The paper introduces a novel loss function enabling abstention during training, improving robustness to various label noise types and aiding in feature learning and data cleaning.

Findings

Significant performance improvements on image benchmarks with label noise

Effective identification of noisy samples for data cleaning

Enhanced feature learning under systematic label noise

Abstract

We introduce a novel method to combat label noise when training deep neural networks for classification. We propose a loss function that permits abstention during training thereby allowing the DNN to abstain on confusing samples while continuing to learn and improve classification performance on the non-abstained samples. We show how such a deep abstaining classifier (DAC) can be used for robust learning in the presence of different types of label noise. In the case of structured or systematic label noise -- where noisy training labels or confusing examples are correlated with underlying features of the data-- training with abstention enables representation learning for features that are associated with unreliable labels. In the case of unstructured (arbitrary) label noise, abstention during training enables the DAC to be used as an effective data cleaner by identifying samples that are likely to have label noise. We provide analytical results on the loss function behavior that enable dynamic adaption of abstention rates based on learning progress during training. We demonstrate the utility of the deep abstaining classifier for various image classification tasks under different types of label noise; in the case of arbitrary label noise, we show significant improvements over previously published results on multiple image benchmarks. Source code is available at https://github.com/thulas/dac-label-noise