Confidence Adaptive Regularization for Deep Learning with Noisy Labels
This addresses the issue of noisy labels in deep learning, which can degrade model performance, but it is incremental as it builds on existing early-learning phenomenon insights.
The paper tackles the problem of deep neural networks memorizing noisy labels by proposing a confidence adaptive regularization method that prevents memorization of mislabeled samples, achieving results comparable to state-of-the-art methods on synthetic and real-world datasets.
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.