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arXiv · 2103.13029

Jo-SRC: A Contrastive Approach for Combating Noisy Labels

Yazhou Yao·Zeren Sun·Chuanyi Zhang·Fumin Shen·Qi Wu·Jian Zhang·Zhenmin Tang
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Abstract

Due to the memorization effect in Deep Neural Networks (DNNs), training with noisy labels usually results in inferior model performance. Existing state-of-the-art methods primarily adopt a sample selection strategy, which selects small-loss samples for subsequent training. However, prior literature tends to perform sample selection within each mini-batch, neglecting the imbalance of noise ratios in different mini-batches. Moreover, valuable knowledge within high-loss samples is wasted. To this end, we propose a noise-robust approach named Jo-SRC (Joint Sample Selection and Model Regularization based on Consistency). Specifically, we train the network in a contrastive learning manner. Predictions from two different views of each sample are used to estimate its "likelihood" of being clean or out-of-distribution. Furthermore, we propose a joint loss to advance the model generalization performance by introducing consistency regularization. Extensive experiments have validated the superiority of our approach over existing state-of-the-art methods.

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Yazhou Yao, Zeren Sun, Chuanyi Zhang, Fumin Shen, Qi Wu, Jian Zhang, Zhenmin Tang. 2021-03-24. Jo-SRC: A Contrastive Approach for Combating Noisy Labels. https://arxiv.org/abs/2103.13029

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