Feature-based Augmentation for Semi-Supervised Learning

Min-Hye Oh, Byung-Gook Park

Abstract: In this paper, we propose a feature-based augmentation, a simple and efficient method for semi-supervised learning, where only a small part of the data is labeled. In semi-supervised learning, input image augmentation is typically known to be a technique for ensuring generalization of unlabeled data. However, unlike general input augmentation(translation, filp, Gaussian noise, etc.), our method adds noise to features that have the most contribution on prediction, generating an augmented features. We call this method ``Feature-based augmentation" because the noise is determined by the network weight itself and augmentation is carried out at the feature level. A prediction by augmented features is used as a target for unlabeled data. The target is stable because it is augmented by the noise based on its extracted features. Feature-based augmentation is applied to semi-supervised learning on SVHN, CIFAR-10 datasets. This method achieved a state-of-the-art error rate. In particular, performance differences from other methods were more pronounced with the smaller the number of labeled data.

Keywords: semi-supervised learning