  We present the Tamed Cross Entropy (TCE) loss function, a robust derivative
of the standard Cross Entropy (CE) loss used in deep learning for
classification tasks. However, unlike other robust losses, the TCE loss is
designed to exhibit the same training properties than the CE loss in noiseless
scenarios. Therefore, the TCE loss requires no modification on the training
regime compared to the CE loss and, in consequence, can be applied in all
applications where the CE loss is currently used. We evaluate the TCE loss
using the ResNet architecture on four image datasets that we artificially
contaminated with various levels of label noise. The TCE loss outperforms the
CE loss in every tested scenario.
