  Deep neural networks (DNNs) have achieved tremendous success in a variety of
applications across many disciplines. Yet, their superior performance comes
with the expensive cost of requiring correctly annotated large-scale datasets.
Moreover, due to DNNs' rich capacity, errors in training labels can hamper
performance. To combat this problem, mean absolute error (MAE) has recently
been proposed as a noise-robust alternative to the commonly-used categorical
cross entropy (CCE) loss. However, as we show in this paper, MAE can perform
poorly with DNNs and challenging datasets. Here, we present a theoretically
grounded set of noise-robust loss functions that can be seen as a
generalization of MAE and CCE. Proposed loss functions can be readily applied
with any existing DNN architecture and algorithm, while yielding good
performance in a wide range of noisy label scenarios. We report results from
experiments conducted with CIFAR-10, CIFAR-100 and FASHION-MNIST datasets and
synthetically generated noisy labels.
