Leveraging Noisy Labels of Nearest Neighbors for Label Correction and Sample Selection

Dealing with noisy labels (LNL) emerges as a critical challenge when applying deep learning (DL) in practical settings. Previous methodologies primarily concentrated on harnessing model predictions to mitigate the impact of noisy labels. Nevertheless, their efficacy is strongly contingent on the accuracy of model predictions, a factor that cannot be assured in the context of LNL. Our empirical analysis shows that in noisy datasets, the spatial information of latent feature representation combined with original noisy labels is more robust than the methods using model predictions. To mitigate the unreliability introduced by model predictions, we propose a novel Feature Representation method, which utilizes noisy labels of nearest neighbors for label Correction and sample Selection (FRCS). Extensive experiments on various benchmark datasets demonstrate the superiority of FRCS compared with SOTA methods. Our codes are available at https://github.com/tianfangjh/FRCS-Noisy-Labels.