Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise
This addresses robustness to label noise for deep learning applications, which is critical due to sources like automatic or adversarial labeling, though it is incremental by building on prior work that assumes no trusted labels.
The paper tackles the problem of training deep neural networks on datasets with severe label noise by leveraging a small subset of trusted, cleanly labeled data, resulting in significant performance gains across vision and NLP tasks compared to existing methods.
The growing importance of massive datasets used for deep learning makes robustness to label noise a critical property for classifiers to have. Sources of label noise include automatic labeling, non-expert labeling, and label corruption by data poisoning adversaries. Numerous previous works assume that no source of labels can be trusted. We relax this assumption and assume that a small subset of the training data is trusted. This enables substantial label corruption robustness performance gains. In addition, particularly severe label noise can be combated by using a set of trusted data with clean labels. We utilize trusted data by proposing a loss correction technique that utilizes trusted examples in a data-efficient manner to mitigate the effects of label noise on deep neural network classifiers. Across vision and natural language processing tasks, we experiment with various label noises at several strengths, and show that our method significantly outperforms existing methods.