Despite the success of deep neural networks (DNNs) in image classification
tasks, the human-level performance relies on massive training data with
high-quality manual annotations, which are expensive and time-consuming to
collect. There exist many inexpensive data sources on the web, but they tend to
contain inaccurate labels. Training on noisy labeled datasets causes
performance degradation because DNNs can easily overfit to the label noise. To
overcome this problem, we propose a noise-tolerant training algorithm, where a
meta-learning update is performed prior to conventional gradient update. The
proposed meta-learning method simulates actual training by generating synthetic
noisy labels, and train the model such that after one gradient update using
each set of synthetic noisy labels, the model does not overfit to the specific
noise. We conduct extensive experiments on the noisy CIFAR-10 dataset and the
Clothing1M dataset. The results demonstrate the advantageous performance of the
proposed method compared to several state-of-the-art baselines.

本文提出一种噪声容忍的训练算法，其中在传统梯度更新之前进行元学习更新来模拟实际训练。通过生成合成噪声标签进行训练，该元学习方法训练模型，以便在使用每个设置的合成噪声标签进行一次梯度更新后，模型不会过度拟合特定的噪声，从而提高深度神经网络的性能。