  Recent studies on the memorization effects of deep neural networks on noisy
labels show that the networks first fit the correctly-labeled training samples
before memorizing the mislabeled samples. Motivated by this early-learning
phenomenon, we propose a novel method to prevent memorization of the mislabeled
samples. Unlike the existing approaches which use the model output to identify
or ignore the mislabeled samples, we introduce an indicator branch to the
original model and enable the model to produce a confidence value for each
sample. The confidence values are incorporated in our loss function which is
learned to assign large confidence values to correctly-labeled samples and
small confidence values to mislabeled samples. We also propose an auxiliary
regularization term to further improve the robustness of the model. To improve
the performance, we gradually correct the noisy labels with a well-designed
target estimation strategy. We provide the theoretical analysis and conduct the
experiments on synthetic and real-world datasets, demonstrating that our
approach achieves comparable results to the state-of-the-art methods.
