  In supervised machine learning, the assumption that training data is labelled
correctly is not always satisfied. In this paper, we investigate an instance of
labelling error for classification tasks in which the dataset is corrupted with
out-of-distribution (OOD) instances: data that does not belong to any of the
target classes, but is labelled as such. We show that detecting and relabelling
certain OOD instances, rather than discarding them, can have a positive effect
on learning. The proposed method uses an auxiliary classifier, trained on data
that is known to be in-distribution, for detection and relabelling. The amount
of data required for this is shown to be small. Experiments are carried out on
the FSDnoisy18k audio dataset, where OOD instances are very prevalent. The
proposed method is shown to improve the performance of convolutional neural
networks by a significant margin. Comparisons with other noise-robust
techniques are similarly encouraging.
