mlsanityData to 7 Oct 2026

Deep Anomaly Detection under Labeling Budget Constraints

arXiv 2302.07832 ↗First posted 15 Feb 2023cs.LG cs.AIcited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 32 of its 79 references that are in the map. First posted on arXiv 15 Feb 2023.

Builds on

32 of 79 references.

References · 32

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20 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 5; 2025: 1; 2026: 1.

9 papers.

  1. 2023DOS: Diverse Outlier Sampling for Out-of-Distribution Detectioncited by 16
  2. 2024Anomaly Detection of Tabular Data Using LLMscited by 12
  3. 2023Model Selection of Anomaly Detectors in the Absence of Labeled Validation Datacited by 8
  4. 2024Multitask Active Learning for Graph Anomaly Detectioncited by 7
  5. 2024Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posteriorcited by 2
  6. 2024Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Datacited by 2
  7. 2024Multi-Normal Prototypes Learning for Weakly Supervised Anomaly Detectioncited by 1
  8. 2025Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detectioncited by 0
  9. 2026Enhancing Tabular Anomaly Detection via Pseudo-Label-Guided Generationcited by 0