Reviews: L_DMI: A Novel Information-theoretic Loss Function for Training Deep Nets Robust to Label Noise

Label noise learning is a hot topic now as the datasets grow bigger and the labels are becoming noisier. How to learn the optimal classifier w.r.t. the clean data from the noisy data is challenging. To guarantee to learn the optimal classifier, many robust learning methods have been proposed. To the best of my knowledge, they all need the information of the transition matrix, learning which could be challenging. This paper proposes the first loss function that is robust to instance-independent label noise without knowing the transition matrix.