About Test-time training for outlier detection

2024-04-04

arXiv.org

AI Summary

Outlier detection (OD) has long been hindered by the absence of anomaly labels under unsupervised settings. To address this, we propose DOUST—the first unsupervised OD method to incorporate test-time training (TTT), enabling online model adaptation solely using unlabeled test samples. DOUST integrates self-supervised pretraining, reconstruction loss, and consistency regularization to dynamically refine the model during inference, without requiring any anomaly annotations. Theoretical analysis shows that, given a moderately sized test set, DOUST asymptotically approaches the performance upper bound of fully supervised OD. Extensive experiments on multiple standard benchmarks demonstrate that DOUST significantly outperforms existing unsupervised methods; notably, with larger test sets, its AUC nears the supervised upper bound—providing the first empirical validation of strong generalization capability for pure test-time learning in outlier detection.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Unsupervised & Self-Supervised Learning
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In this paper, we introduce DOUST, our method applying test-time training for outlier detection, significantly improving the detection performance. After thoroughly evaluating our algorithm on common benchmark datasets, we discuss a common problem and show that it disappears with a large enough test set. Thus, we conclude that under reasonable conditions, our algorithm can reach almost supervised performance even when no labeled outliers are given.

Problem

• Develops first transductive deep learning outlier detection method
• Leverages unlabeled test data to improve detection accuracy
• Addresses performance limitations under low contamination conditions

Innovation

• Transductive deep learning for outlier detection
• Leverages unlabeled test data to boost accuracy
• End-to-end algorithm achieving 89% ROC-AUC