  Deep neural networks (DNNs) exhibit great success on many tasks with the help
of large-scale well annotated datasets. However, labeling large-scale data can
be very costly and error-prone so that it is difficult to guarantee the
annotation quality (i.e., having noisy labels). Training on these noisy labeled
datasets may adversely deteriorate their generalization performance. Existing
methods either rely on complex training stage division or bring too much
computation for marginal performance improvement. In this paper, we propose a
Temporal Calibrated Regularization (TCR), in which we utilize the original
labels and the predictions in the previous epoch together to make DNN inherit
the simple pattern it has learned with little overhead. We conduct extensive
experiments on various neural network architectures and datasets, and find that
it consistently enhances the robustness of DNNs to label noise.
