mlsanityData to 7 Oct 2026

TracInAD: Measuring Influence for Anomaly Detection

arXiv 2205.01362 ↗First posted 3 May 2022cs.LGcited by 8

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Abstract (arXiv)

As with many other tasks, neural networks prove very effective for anomaly detection purposes. However, very few deep-learning models are suited for detecting anomalies on tabular datasets. This paper proposes a novel methodology to flag anomalies based on TracIn, an influence measure initially introduced for explicability purposes. The proposed methods can serve to augment any unsupervised deep anomaly detection method. We test our approach using Variational Autoencoders and show that the average influence of a subsample of training points on a test point can serve as a proxy for abnormality. Our model proves to be competitive in comparison with state-of-the-art approaches: it achieves comparable or better performance in terms of detection accuracy on medical and cyber-security tabular benchmark data.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 15 of its 23 references that are in the map. First posted on arXiv 3 May 2022.

Builds on

15 of 23 references.

References · 15

  1. 2013Auto-Encoding Variational Bayescited by 3,980
  2. 2017Understanding Black-box Predictions via Influence Functionscited by 1,714
  3. 2020Estimating Training Data Influence by Tracing Gradient Descentcited by 565
  4. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 423
  5. 2019Deep Semi-Supervised Anomaly Detectioncited by 290
  6. 2020Classification-Based Anomaly Detection for General Datacited by 210
  7. 2018Representer Point Selection for Explaining Deep Neural Networkscited by 187
  8. 2020Learning and Evaluating Representations for Deep One-class Classificationcited by 133
  9. 2020DROCC: Deep Robust One-Class Classificationcited by 121
  10. 2021Neural Transformation Learning for Deep Anomaly Detection Beyond Imagescited by 98
  11. 2019On Second-Order Group Influence Functions for Black-Box Predictionscited by 78
  12. 2020RelatIF: Identifying Explanatory Training Examples via Relative Influencecited by 28
3 more
  1. 2021Understanding Instance-based Interpretability of Variational Auto-Encoderscited by 21
  2. 2020Simple and Effective Prevention of Mode Collapse in Deep One-Class Classificationcited by 16
  3. 2018Efficient Anomaly Detection via Matrix Sketchingcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 4; 2024: 1; 2025: 1; 2026: 1.

8 papers.

  1. 2022Training Data Influence Analysis and Estimation: A Surveycited by 134
  2. 2023Beyond Individual Input for Deep Anomaly Detection on Tabular Datacited by 14
  3. 2023Sample based Explanations via Generalized Representerscited by 10
  4. 2023Comparative Evaluation of Anomaly Detection Methods for Fraud Detection in Online Credit Card Paymentscited by 4
  5. 2023Representer Point Selection for Explaining Regularized High-dimensional Modelscited by 1
  6. 2024Enhancing High-Energy Particle Physics Collision Analysis through Graph Data Attribution Techniquescited by 1
  7. 2025A Comparative Analysis of Influence Signals for Data Debuggingcited by 0
  8. 2026TaskFusion: Continual Anomaly Detection for Heterogeneous Tabular Datacited by 0