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Noisy labels present a significant challenge in deep learning because modelsare prone to overfitting. This problem has driven the development ofsophisticated techniques to address the issue, with one critical componentbeing the selection of clean and noisy label samples. Selecting noisy labelsamples is commonly based on the small-loss hypothesis or on feature-basedsampling, but we present empirical evidence that shows that both strategiesstruggle to differentiate between noisy label and hard samples, resulting inrelatively large proportions of samples falsely selected as clean. To addressthis limitation, we propose a novel peer-agreement based sample selection(PASS). An automated thresholding technique is then applied to the agreementscore to select clean and noisy label samples. PASS is designed to be easilyintegrated into existing noisy label robust frameworks, and it involvestraining a set of classifiers in a round-robin fashion, with peer models usedfor sample selection. In the experiments, we integrate our PASS with severalstate-of-the-art (SOTA) models, including InstanceGM, DivideMix, SSR, FaMUS,AugDesc, and C2D, and evaluate their effectiveness on several noisy labelbenchmark datasets, such as CIFAR-100, CIFAR-N, Animal-10N, Red Mini-Imagenet,Clothing1M, Mini-Webvision, and Imagenet. Our results demonstrate that our newsample selection approach improves the existing SOTA results of algorithms.

PASS: Peer-Agreement based Sample Selection for training with Noisy Labels