  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.
