Robust Noisy Label Learning via Two-Stream Sample Distillation

• URL: http://arxiv.org/abs/2404.10499v1
• Date: Tue, 16 Apr 2024 12:18:08 GMT
• Title: Robust Noisy Label Learning via Two-Stream Sample Distillation
• Authors: Sihan Bai, Sanping Zhou, Zheng Qin, Le Wang, Nanning Zheng,
• Abstract summary: Noisy label learning aims to learn robust networks under the supervision of noisy labels. We design a simple yet effective sample selection framework, termed Two-Stream Sample Distillation (TSSD) This framework can extract more high-quality samples with clean labels to improve the robustness of network training.
• Score: 48.73316242851264
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Noisy label learning aims to learn robust networks under the supervision of noisy labels, which plays a critical role in deep learning. Existing work either conducts sample selection or label correction to deal with noisy labels during the model training process. In this paper, we design a simple yet effective sample selection framework, termed Two-Stream Sample Distillation (TSSD), for noisy label learning, which can extract more high-quality samples with clean labels to improve the robustness of network training. Firstly, a novel Parallel Sample Division (PSD) module is designed to generate a certain training set with sufficient reliable positive and negative samples by jointly considering the sample structure in feature space and the human prior in loss space. Secondly, a novel Meta Sample Purification (MSP) module is further designed to mine adequate semi-hard samples from the remaining uncertain training set by learning a strong meta classifier with extra golden data. As a result, more and more high-quality samples will be distilled from the noisy training set to train networks robustly in every iteration. Extensive experiments on four benchmark datasets, including CIFAR-10, CIFAR-100, Tiny-ImageNet, and Clothing-1M, show that our method has achieved state-of-the-art results over its competitors.

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