mlsanityData to 7 Oct 2026

Contrastive Predictive Coding for Anomaly Detection

arXiv 2107.07820 ↗First posted 16 Jul 2021cs.CV cs.LGcited by 10

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Abstract (arXiv)

Reliable detection of anomalies is crucial when deploying machine learning models in practice, but remains challenging due to the lack of labeled data. To tackle this challenge, contrastive learning approaches are becoming increasingly popular, given the impressive results they have achieved in self-supervised representation learning settings. However, while most existing contrastive anomaly detection and segmentation approaches have been applied to images, none of them can use the contrastive losses directly for both anomaly detection and segmentation. In this paper, we close this gap by making use of the Contrastive Predictive Coding model (arXiv:1807.03748). We show that its patch-wise contrastive loss can directly be interpreted as an anomaly score, and how this allows for the creation of anomaly segmentation masks. The resulting model achieves promising results for both anomaly detection and segmentation on the challenging MVTec-AD dataset.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 26 of its 31 references that are in the map. First posted on arXiv 16 Jul 2021.

Builds on

26 of 31 references.

References · 26

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  3. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  4. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
  5. 2016Identity Mappings in Deep Residual Networkscited by 1,960
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  9. 2020Prototypical Contrastive Learning of Unsupervised Representationscited by 636
  10. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 591
  11. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  12. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 529
14 more
  1. 2020CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instancescited by 441
  2. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 423
  3. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
  4. 2019Deep Semi-Supervised Anomaly Detectioncited by 290
  5. 2018Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoderscited by 263
  6. 2020Contrastive Training for Improved Out-of-Distribution Detectioncited by 186
  7. 2020Learning and Evaluating Representations for Deep One-class Classificationcited by 133
  8. 2020Explainable Deep One-Class Classificationcited by 126
  9. 2019Towards Visually Explaining Variational Autoencoderscited by 113
  10. 2021Neural Transformation Learning for Deep Anomaly Detection Beyond Imagescited by 98
  11. 2019Putting An End to End-to-End: Gradient-Isolated Learning of Representationscited by 85
  12. 2020Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Imagescited by 52
  13. 2020Driver Anomaly Detection: A Dataset and Contrastive Learning Approachcited by 41
  14. 2020The Clever Hans Effect in Anomaly Detectioncited by 13

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Cited by year

Citing papers per year: 2022: 3; 2023: 3; 2024: 2; 2025: 2.

10 papers.

  1. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 147
  2. 2022Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Surveycited by 67
  3. 2023IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturingcited by 64
  4. 2023Explainable Time Series Anomaly Detection using Masked Latent Generative Modelingcited by 15
  5. 2022Detecting Anomalies within Time Series using Local Neural Transformationscited by 12
  6. 2022Deep Contrastive One-Class Time Series Anomaly Detectioncited by 10
  7. 2024Universal Novelty Detection Through Adaptive Contrastive Learningcited by 9
  8. 2024Incomplete Multimodal Industrial Anomaly Detection via Cross-Modal Distillationcited by 6
  9. 2025RoCA: Robust Contrastive One-class Time Series Anomaly Detection with Contaminated Datacited by 4
  10. 2025AutoSciDACT: Automated Scientific Discovery through Contrastive Embedding and Hypothesis Testingcited by 2