Combating noisy labels by agreement: A joint training method with co-regularization
This addresses the challenge of noisy labels in weakly supervised learning, which is a practical issue in many real-world applications, and it is an incremental improvement over existing disagreement-based methods.
The paper tackles the problem of deep learning with noisy labels by proposing JoCoR, a joint training method with co-regularization that reduces network diversity, and it demonstrates superior performance over state-of-the-art approaches on benchmark datasets like MNIST, CIFAR-10, CIFAR-100, and Clothing1M.
Deep Learning with noisy labels is a practically challenging problem in weakly supervised learning. The state-of-the-art approaches "Decoupling" and "Co-teaching+" claim that the "disagreement" strategy is crucial for alleviating the problem of learning with noisy labels. In this paper, we start from a different perspective and propose a robust learning paradigm called JoCoR, which aims to reduce the diversity of two networks during training. Specifically, we first use two networks to make predictions on the same mini-batch data and calculate a joint loss with Co-Regularization for each training example. Then we select small-loss examples to update the parameters of both two networks simultaneously. Trained by the joint loss, these two networks would be more and more similar due to the effect of Co-Regularization. Extensive experimental results on corrupted data from benchmark datasets including MNIST, CIFAR-10, CIFAR-100 and Clothing1M demonstrate that JoCoR is superior to many state-of-the-art approaches for learning with noisy labels.