Unifying semi-supervised and robust learning by mixup
Supervised deep learning methods require cleanly labeled large-scale datasets, but collecting such data is difficult and sometimes impossible.There exist two popular frameworks to alleviate this problem: semi-supervised learning and robust learning to label noise.Although these frameworks relax the restriction of supervised learning, they are studied independently.Hence, the training scheme that is suitable when only small cleanly-labeled data are available remains unknown.In this study, we consider learning from bi-quality data as a generalization of these studies, in which a small portion of data is cleanly labeled, and the rest is corrupt.Under this framework, we compare recent algorithms for semi-supervised and robust learning.The results suggest that semi-supervised learning outperforms robust learning with noisy labels.We also propose a training strategy for mixing mixup techniques to learn from such bi-quality data effectively.
We propose to compare semi-supervised and robust learning to noisy label under a shared settingThe authors propose a strategy based on mixup for training a model in a formal setting that includes the semi-supervised and the robust learning tasks as special cases.