Papers › Semi-supervised novelty detection using ensembles with regularized disagreement

Semi-supervised novelty detection using ensembles with regularized disagreement

10 Dec 2020arXiv:2012.05825archive 2025-07-28

Alexandru Ţifrea, Eric Stavarache, Fanny Yang

Deep neural networks often predict samples with high confidence even when they come from unseen classes and should instead be flagged for expert evaluation. Current novelty detection algorithms cannot reliably identify such near OOD points unless they have access to labeled data that is similar to these novel samples. In this paper, we develop a new ensemble-based procedure for semi-supervised novelty detection (SSND) that successfully leverages a mixture of unlabeled ID and novel-class samples to achieve good detection performance. In particular, we show how to achieve disagreement only on OOD data using early stopping regularization. While we prove this fact for a simple data distribution, our extensive experiments suggest that it holds true for more complex scenarios: our approach significantly outperforms state-of-the-art SSND methods on standard image data sets (SVHN/CIFAR-10/CIFAR-100) and medical image data sets with only a negligible increase in computation cost.

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Tasks

Novelty DetectionOut of Distribution (OOD) DetectionOut-of-Distribution Detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Out-of-Distribution Detection CIFAR-10 vs CIFAR-10.1 ERD (ResNet18) AUROC 91.4 #1 of 1 Archive leaderboard report
Out-of-Distribution Detection CIFAR-10 vs CIFAR-100 ERD (ResNet18) AUROC 95.1 #8 of 14 Archive leaderboard report
Out-of-Distribution Detection CIFAR-100 vs CIFAR-10 ERD (ResNet18) AUROC 94.3 #8 of 14 Archive leaderboard report

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