Jo-SRC: A Contrastive Approach for Combating Noisy Labels

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.

accepted by IEEE Conference on Computer Vision and Pattern Recognition, 2021

References in corpus (7)

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• A Simple Framework for Contrastive Learning of Visual Representations
• Bootstrap your own latent: A new approach to self-supervised Learning
• Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
• Training Convolutional Networks with Noisy Labels
• DivideMix: Learning with Noisy Labels as Semi-supervised Learning
• Unsupervised Label Noise Modeling and Loss Correction