UNKNOWN CLASS LABEL CLEANING FOR LEARNING WITH OPEN-SET NOISY LABELS

Deep neural networks (DNNs) trained on large-scale annotated datasets have achieved impressive results in the area of image classification. Many large-scale datasets have been collected from websites; however, such data are inevitably corrupted with noise. In this study, we researched the open-set noisy label problem, where some outliers are contained in a dataset and annotated through a noisy label but do not belong to any class of training data. To address this problem, we propose a novel unknown class label cleaning framework for the training of DNNs with open-set noisy labels. In addition to general image classification, we also estimate the probability of an input being from an unknown class by assigning a pseudo unknown label to all of the data and correct these labels through an alternating update of the network parameters and labels. The results of experiments conducted on the noisy CIFAR-10 datasets demonstrate that our approach can robustly train DNNs with a high proportion of noisy labels.