Enhanced Meta Label Correction for Coping with Label Corruption

Mitchell Keren Taraday, Chaim Baskin; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 16295-16304

Deep Neural Networks (DNNs) have revolutionized visual classification tasks over the last decade. The training phase of deep-learning-based algorithms, however, often requires a vast amount of reliable annotated data. While reliability collecting such amount of labeled data usually yields to an exhaustive, expensive process, for many applications, acquiring massive datasets with imperfect annotations is straightforward. For instance, crawling search engines and online websites can generate a boatload amount of noisy labeled data. Hence, solving the problem of learning with noisy labels (LNL) is of paramount importance. Traditional LNL methods have successfully handled datasets with artificially injected noise, but they still fall short of adequately handling real-world noise. With the increasing use of meta-learning in the diverse fields of machine learning, researchers have tried to leverage auxiliary small clean datasets to meta-correct the training labels. Nonetheless, existing meta-label correction approaches are not fully exploiting their potential. In this study, we propose EMLC, an enhanced meta-label correction approach for the LNL problem. We re-examine the meta-learning process and introduce faster and more accurate meta-gradient derivations. We propose a novel teacher architecture tailored explicitly for the LNL problem, equipped with novel training objectives. EMLC outperforms prior approaches and achieves state-of-the-art results in all standard benchmarks. Notably, EMLC enhances the previous art on the noisy real-world dataset Clothing1M by 0.87%. Our publicly available code can be found at the following link: https://github.com/iccv23anonymous/Enhanced-Meta-Label-Correction