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。

从含有噪声标签的数据中学习如何学习

Learning to Learn from Noisy Labeled Data

Collecting large training datasets, annotated with high-quality labels, is
costly and time-consuming. This paper proposes a novel framework for training
deep convolutional neural networks from noisy labeled datasets that can be
obtained cheaply. The problem is formulated using an undirected graphical model
that represents the relationship between noisy and clean labels, trained in a
semi-supervised setting. In our formulation, the inference over latent clean
labels is tractable and is regularized during training using auxiliary sources
of information. The proposed model is applied to the image labeling problem and
is shown to be effective in labeling unseen images as well as reducing label
noise in training on CIFAR-10 and MS COCO datasets.

该论文提出了一种基于无标签干扰数据集训练深度卷积神经网络的新型框架，并使用一个无向图模型来描述干净和嘈杂标签之间的关系，在监督学习过程中学习这个模型。该模型在图像标注问题上应用，并在 CIFAR-10 和 MS COCO 数据集上展示出有效的标注效果和在训练中实现了减少标签噪声的效果。