Large-scale datasets possessing clean label annotations are crucial for
training Convolutional Neural Networks (CNNs). However, labeling large-scale
data can be very costly and error-prone, and even high-quality datasets are
likely to contain noisy (incorrect) labels. Existing works usually employ a
closed-set assumption, whereby the samples associated with noisy labels possess
a true class contained within the set of known classes in the training data.
However, such an assumption is too restrictive for many applications, since
samples associated with noisy labels might in fact possess a true class that is
not present in the training data. We refer to this more complex scenario as the
\textbf{open-set noisy label} problem and show that it is nontrivial in order
to make accurate predictions. To address this problem, we propose a novel
iterative learning framework for training CNNs on datasets with open-set noisy
labels. Our approach detects noisy labels and learns deep discriminative
features in an iterative fashion. To benefit from the noisy label detection, we
design a Siamese network to encourage clean labels and noisy labels to be
dissimilar. A reweighting module is also applied to simultaneously emphasize
the learning from clean labels and reduce the effect caused by noisy labels.
Experiments on CIFAR-10, ImageNet and real-world noisy (web-search) datasets
demonstrate that our proposed model can robustly train CNNs in the presence of
a high proportion of open-set as well as closed-set noisy labels.

本研究提出了一种适用于具有无法识别真实类别的嘈杂标签开放集问题的卷积神经网络（CNN）训练的新的迭代学习框架，包括噪声标签检测和清晰标签的权重设计。经过实验证明，该模型能够在高比例的开放集和闭集噪声标签情况下鲁棒地训练 CNNs。