mlsanityData to 14 Oct 2026

Set Features for Anomaly Detection

arXiv 2311.14773 ↗First posted 24 Nov 2023cs.CV cs.LGcited by 3

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Abstract (arXiv)

This paper proposes to use set features for detecting anomalies in samples that consist of unusual combinations of normal elements. Many leading methods discover anomalies by detecting an unusual part of a sample. For example, state-of-the-art segmentation-based approaches, first classify each element of the sample (e.g., image patch) as normal or anomalous and then classify the entire sample as anomalous if it contains anomalous elements. However, such approaches do not extend well to scenarios where the anomalies are expressed by an unusual combination of normal elements. In this paper, we overcome this limitation by proposing set features that model each sample by the distribution of its elements. We compute the anomaly score of each sample using a simple density estimation method, using fixed features. Our approach outperforms the previous state-of-the-art in image-level logical anomaly detection and sequence-level time series anomaly detection.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 35 of its 65 references that are in the map. First posted on arXiv 24 Nov 2023.

Builds on

35 of 65 references.

References · 35

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  2. 2017Billion-scale similarity search with GPUscited by 2,497
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  4. 2017Deep Setscited by 1,421
  5. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  6. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 594
  7. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  8. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 531
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  11. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 425
  12. 2022SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentationcited by 400
23 more
  1. 2019ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernelscited by 339
  2. 2020Sub-Image Anomaly Detection with Deep Pyramid Correspondencescited by 300
  3. 2019Deep Semi-Supervised Anomaly Detectioncited by 291
  4. 2020Multiresolution Knowledge Distillation for Anomaly Detectioncited by 285
  5. 2018Deep Anomaly Detection Using Geometric Transformationscited by 269
  6. 2016Learning Representations for Automatic Colorizationcited by 251
  7. 2016LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detectioncited by 215
  8. 2020Classification-Based Anomaly Detection for General Datacited by 211
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  10. 2020A review on outlier/anomaly detection in time series datacited by 189
  11. 2020PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentationcited by 175
  12. 2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latenciescited by 160
  13. 2020DROCC: Deep Robust One-Class Classificationcited by 121
  14. 2020Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detectioncited by 115
  15. 2021Neural Transformation Learning for Deep Anomaly Detection Beyond Imagescited by 99
  16. 2021Mean-Shifted Contrastive Loss for Anomaly Detectioncited by 88
  17. 2018Learning Deep Features for One-Class Classificationcited by 76
  18. 2022The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localizationcited by 57
  19. 2015The Radon cumulative distribution transform and its application to image classificationcited by 28
  20. 2017Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Networkcited by 28
  21. 2021Transformaly -- Two (Feature Spaces) Are Better Than Onecited by 25
  22. 2022Anomaly Detection Requires Better Representationscited by 15
  23. 2023No Free Lunch: The Hazards of Over-Expressive Representations in Anomaly Detectioncited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025SALAD -- Semantics-Aware Logical Anomaly Detectioncited by 4
  2. 2026UniSLAD: A Unified Framework for Structural and Logical Industrial Visual Anomaly Detectioncited by 1
  3. 2026Training-Free Logical and Structural Anomaly Detection via Calibrated Fusioncited by 0