Pairwise Similarity Distribution Clustering for Noisy Label Learning
Sihan Bai
Noisy label learning aims to train deep neural networks using a large amount
of samples with noisy labels, whose main challenge comes from how to deal with
the inaccurate supervision caused by wrong labels. Existing works either take
the label correction or sample selection paradigm to involve more samples with
accurate labels into the training process. In this paper, we propose a simple
yet effective sample selection algorithm, termed as Pairwise Similarity
Distribution Clustering~(PSDC), to divide the training samples into one clean
set and another noisy set, which can power any of the off-the-shelf
semi-supervised learning regimes to further train networks for different
downstream tasks. Specifically, we take the pairwise similarity between sample
pairs to represent the sample structure, and the Gaussian Mixture Model~(GMM)
to model the similarity distribution between sample pairs belonging to the same
noisy cluster, therefore each sample can be confidently divided into the clean
set or noisy set. Even under severe label noise rate, the resulting data
partition mechanism has been proved to be more robust in judging the label
confidence in both theory and practice. Experimental results on various
benchmark datasets, such as CIFAR-10, CIFAR-100 and Clothing1M, demonstrate
significant improvements over state-of-the-art methods.