Semi-supervised learning (SSL) methods assume that labeled data, unlabeled data and test
data are from the same distribution. Open-set semi-supervised learning (Open-set SSL)
considers a more practical scenario, where unlabeled data and test data contain new
categories (outliers) not observed in labeled data (inliers). Most previous works focused on
outlier detection via binary classifiers, which suffer from insufficient scalability and inability to
distinguish different types of uncertainty. In this paper, we propose a novel framework …

Semi-supervised learning (SSL) methods assume that labeled data, unlabeled data and test
data are from the same distribution. Open-set semi-supervised learning (Open-set SSL) con-
siders a more practical scenario, where unlabeled data and test data contain new categories
(outliers) not observed in labeled data (inliers). Most previous works focused on out-lier
detection via binary classifiers, which suffer from insufficient scalability and inability to
distinguish different types of uncertainty. In this paper, we propose a novel framework …