Quadripartite: Tackle Noisy Labels by Better Utilizing Uncertain Samples

Xiaoxuan Cui, Xuefeng Liang, Chang Cao, Guanghui Shi, Nesma El-haddar

Abstract: To tackle the noisy label problem, sample selection methods have demonstrated particular advantages. An emerging method, named Tripartite, was proposed to divide the data into clean, uncertain, and noisy subsets and applies a low-weight training strategy to the uncertain subset to alleviate the harm of noisy labels. However, we find the uncertain subset selected by the Tripartite contains many noisy samples as well, which considerably lowers the performance of models even when using low-weight training. Therefore, we propose the Quadripartite, which further divides the uncertain subset into an uncertain clean and an uncertain noisy subset. The data visualization shows that these two new subsets have a high sample selection quality. It addresses a tough challenge that most sample selection methods face: how to reliably select the hard clean samples. The Quadripartite employs a weighted learning on the uncertain clean subset to maximize the utilization of clean samples, while dropping the uncertain noisy subset during the training stage to mitigate the negative effects caused by noisy samples. Extensive experiments show Quadripartite outperforms the state-of-the-art approaches on four benchmark datasets.

External IDs:dblp:conf/ijcnn/CuiLCSE24