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Deep neural networks (DNNs) exhibit great success on many tasks with the helpof large-scale well annotated datasets. However, labeling large-scale data canbe very costly and error-prone so that it is difficult to guarantee theannotation quality (i.e., having noisy labels). Training on these noisy labeleddatasets may adversely deteriorate their generalization performance. Existingmethods either rely on complex training stage division or bring too muchcomputation for marginal performance improvement. In this paper, we propose aTemporal Calibrated Regularization (TCR), in which we utilize the originallabels and the predictions in the previous epoch together to make DNN inheritthe simple pattern it has learned with little overhead. We conduct extensiveexperiments on various neural network architectures and datasets, and find thatit consistently enhances the robustness of DNNs to label noise.

Temporal Calibrated Regularization for Robust Noisy Label Learning