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.

本文探讨了监督式机器学习中标注错误的一种情况，即数据集受到了分布外的污染，我们提出的方法使用辅助分类器来检测和重新标注这些污染数据，并在 FSDnoisy18k 音频数据集上实验，结果表明该方法显著提高了卷积神经网络的性能，与其他噪声抗干扰技术的比较也是鼓舞人心的。