Delving into Noisy Label Detection with Clean Data

A critical element of learning with noisy labels is noisy label detection. Notably, numerous previous works assume that no source of labels can be clean in a noisy label detection context. In this work, we relax this assumption and assume that a small subset of the training data is clean, which enables substantial noisy label detection performance gains. Specifically, we propose a novel framework that leverages clean data by framing the problem of noisy label detection with clean data as a multiple hypothesis testing problem. Moreover, we propose BHN, a simple yet effective approach for noisy label detection that integrates the Benjamini-Hochberg (BH) procedure into deep neural networks. BHN achieves state-ofthe-art performance and outperforms baselines by 28.48% in terms of false discovery rate (FDR) and by 18.99% in terms of F1 on CIFAR-10. Extensive ablation studies further demonstrate the superiority of BHN. Our code is available at https://github.com/ChenglinYu/BHN .