Learning With Neighbor Consistency for Noisy Labels

Ahmet Iscen, Jack Valmadre, Anurag Arnab, Cordelia Schmid; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 4672-4681

Abstract


Recent advances in deep learning have relied on large, labelled datasets to train high-capacity models. However, collecting large datasets in a time- and cost-efficient manner often results in label noise. We present a method for learning from noisy labels that leverages similarities between training examples in feature space, encouraging the prediction of each example to be similar to its nearest neighbours. Compared to training algorithms that use multiple models or distinct stages, our approach takes the form of a simple, additional regularization term. It can be interpreted as an inductive version of the classical, transductive label propagation algorithm. We thoroughly evaluate our method on datasets evaluating both synthetic (CIFAR-10, CIFAR-100) and realistic (mini-WebVision, WebVision, Clothing1M, mini-ImageNet-Red) noise, and achieve competitive or state-of-the-art accuracies across all of them.

Related Material


[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Iscen_2022_CVPR, author = {Iscen, Ahmet and Valmadre, Jack and Arnab, Anurag and Schmid, Cordelia}, title = {Learning With Neighbor Consistency for Noisy Labels}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {4672-4681} }