Training deep neural-networks based on unreliable labels

Abstract

In this study we address the problem of training a neural network based on data with unreliable labels. We introduce an extra noise layer by assuming that the observed labels were created from the true labels by passing through a noisy channel whose parameters are unknown. We propose a method that simultaneously learns both the neural network parameters and the noise distribution. The proposed method is compared to standard back-propagation neural-network training that ignores the existence of wrong labels. The improved classification performance of the method is illustrated on several standard classification tasks. In particular we show that in some cases our approach can be beneficial even when the labels are set manually and assumed to be error-free.