Robust Noisy Label Learning via Two-Stream Sample Distillation
Sihan Bai Sanping Zhou Zheng Qin Le Wang Nanning Zheng
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.