Title: Novelty detection using ensembles with regularized disagreement. Abstract: Despite their excellent performance on in-distribution (ID) data, deep neural networks often confidently predict on out-of-distribution (OOD) samples that come from novel classes instead of flagging them for expert evaluation. Even though conventional OOD detection algorithms can distinguish far OOD samples, current methods that can identify near OOD samples require training with labeled data that is very similar to these near OOD samples. In turn, we develop a new ensemble-based procedure for \emph{semi-supervised novelty detection} (SSND) that only utilizes a mixture of unlabeled ID and OOD samples to achieve good detection performance on near OOD data. It crucially relies on regularization to promote diversity on the OOD data while preserving agreement on ID data. Extensive comparisons of our approach to state-of-the-art SSND methods on standard image data sets (SVHN/CIFAR-10/CIFAR-100) and medical image data sets reveal significant gains with negligible increase in computational cost.
