CrossSplit: Mitigating Label Noise Memorization through Data Splitting

• URL: http://arxiv.org/abs/2212.01674v2
• Date: Wed, 26 Apr 2023 15:33:27 GMT
• Title: CrossSplit: Mitigating Label Noise Memorization through Data Splitting
• Authors: Jihye Kim, Aristide Baratin, Yan Zhang, Simon Lacoste-Julien
• Abstract summary: We propose a novel training procedure to mitigate the memorization of noisy labels, called CrossSplit. Experiments on CIFAR-10, CIFAR-100, Tiny-ImageNet and mini-WebVision datasets demonstrate that our method can outperform the current state-of-the-art in a wide range of noise ratios.
• Score: 25.344386272010397
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We approach the problem of improving robustness of deep learning algorithms in the presence of label noise. Building upon existing label correction and co-teaching methods, we propose a novel training procedure to mitigate the memorization of noisy labels, called CrossSplit, which uses a pair of neural networks trained on two disjoint parts of the labelled dataset. CrossSplit combines two main ingredients: (i) Cross-split label correction. The idea is that, since the model trained on one part of the data cannot memorize example-label pairs from the other part, the training labels presented to each network can be smoothly adjusted by using the predictions of its peer network; (ii) Cross-split semi-supervised training. A network trained on one part of the data also uses the unlabeled inputs of the other part. Extensive experiments on CIFAR-10, CIFAR-100, Tiny-ImageNet and mini-WebVision datasets demonstrate that our method can outperform the current state-of-the-art in a wide range of noise ratios.

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