  Training accurate deep neural networks (DNNs) in the presence of noisy labels
is an important and challenging task. Though a number of approaches have been
proposed for learning with noisy labels, many open issues remain. In this
paper, we show that DNN learning with Cross Entropy (CE) exhibits overfitting
to noisy labels on some classes ("easy" classes), but more surprisingly, it
also suffers from significant under learning on some other classes ("hard"
classes). Intuitively, CE requires an extra term to facilitate learning of hard
classes, and more importantly, this term should be noise tolerant, so as to
avoid overfitting to noisy labels. Inspired by the symmetric KL-divergence, we
propose the approach of \textbf{Symmetric cross entropy Learning} (SL),
boosting CE symmetrically with a noise robust counterpart Reverse Cross Entropy
(RCE). Our proposed SL approach simultaneously addresses both the under
learning and overfitting problem of CE in the presence of noisy labels. We
provide a theoretical analysis of SL and also empirically show, on a range of
benchmark and real-world datasets, that SL outperforms state-of-the-art
methods. We also show that SL can be easily incorporated into existing methods
in order to further enhance their performance.
