Deep Anomaly Detection under Labeling Budget Constraints
This work addresses the challenge of efficient anomaly detection in domains like medical diagnostics or fraud detection where labeling is costly, though it appears incremental by building on existing semi-supervised methods.
The paper tackles the problem of improving anomaly detection performance with limited expert feedback by establishing theoretical conditions for score generalization and proposing an optimal labeling strategy and learning framework. Experiments across image, tabular, and video datasets show state-of-the-art results under labeling budget constraints.
Selecting informative data points for expert feedback can significantly improve the performance of anomaly detection (AD) in various contexts, such as medical diagnostics or fraud detection. In this paper, we determine a set of theoretical conditions under which anomaly scores generalize from labeled queries to unlabeled data. Motivated by these results, we propose a data labeling strategy with optimal data coverage under labeling budget constraints. In addition, we propose a new learning framework for semi-supervised AD. Extensive experiments on image, tabular, and video data sets show that our approach results in state-of-the-art semi-supervised AD performance under labeling budget constraints.