  Deep neural networks are susceptible to label noise. Existing methods to
improve robustness, such as meta-learning and regularization, usually require
significant change to the network architecture or careful tuning of the
optimization procedure. In this work, we propose a simple hierarchical approach
that incorporates a label hierarchy when training the deep learning models. Our
approach requires no change of the network architecture or the optimization
procedure. We investigate our hierarchical network through a wide range of
simulated and real datasets and various label noise types. Our hierarchical
approach improves upon regular deep neural networks in learning with label
noise. Combining our hierarchical approach with pre-trained models achieves
state-of-the-art performance in real-world noisy datasets.
