Safe semi-supervised learning using a bayesian neural network

Abstract

Semi-supervised learning attempts to use a large set of unlabeled data to increase the pre-diction accuracy of machine learning models when the amount of labeled data is limited. However, in realistic cases, unlabeled data may worsen performance because they contain out-of-distribution (OOD) data that differ from the labeled data. To address this issue, safe semi-supervised deep learning has recently been presented. This study suggests a new safe semi-supervised algorithm that uses an uncertainty-aware Bayesian neural network. Our proposed method, safe uncertainty-based consistency training (SafeUC), uses Bayesian uncertainty to minimize the harmful effects caused by unlabeled OOD examples. The pro-posed method improves the model's generalization performance by regularizing the net-work for consistency against uncertain noise. Moreover, to avoid uncertain prediction results, the proposed method includes a practical inference tip based on a well -calibrated uncertainty. The effectiveness of the proposed method is demonstrated in the experimental results on CIFAR-10 and SVHN by showing that it achieved state-of-the-art performance for all semi-supervised learning tasks with OOD data presence rates.(c) 2022 Elsevier Inc. All rights reserved.