Yu-Ting Chou

Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels

In weakly supervised learning, unbiased risk estimator(URE) is a powerful tool for training classifiers when training and test data are drawn from different distributions. Nevertheless, UREs lead to overfitting in many problem settings when the models are complex like deep networks. In this paper, w…