Noisy labels can impair the performance of deep neural networks. To tackle this problem, in this paper, we propose a new method for filtering label noise. Unlike most existing methods relying on the posterior probability of a noisy classifier, we focus on the much richer spatial behavior of data in the latent representational space. By leveraging the high-order topological information of data, we are able to collect most of the clean data and train a high-quality model. Theoretically we prove that this topological approach is guaranteed to collect the clean data with high probability. Empirical results show that our method outperforms the state-of-the-arts and is robust to a broad spectrum of noise types and levels.
@article{arxiv.2012.04835,
title = {A Topological Filter for Learning with Label Noise},
author = {Pengxiang Wu and Songzhu Zheng and Mayank Goswami and Dimitris Metaxas and Chao Chen},
journal= {arXiv preprint arXiv:2012.04835},
year = {2022}
}