mlsanityData to 7 Oct 2026

Continual Learning Approaches for Anomaly Detection

arXiv 2212.11192 ↗First posted 21 Dec 2022cs.CVcited by 9

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Abstract (arXiv)

Anomaly Detection is a relevant problem that arises in numerous real-world applications, especially when dealing with images. However, there has been little research for this task in the Continual Learning setting. In this work, we introduce a novel approach called SCALE (SCALing is Enough) to perform Compressed Replay in a framework for Anomaly Detection in Continual Learning setting. The proposed technique scales and compresses the original images using a Super Resolution model which, to the best of our knowledge, is studied for the first time in the Continual Learning setting. SCALE can achieve a high level of compression while maintaining a high level of image reconstruction quality. In conjunction with other Anomaly Detection approaches, it can achieve optimal results. To validate the proposed approach, we use a real-world dataset of images with pixel-based anomalies, with the scope to provide a reliable benchmark for Anomaly Detection in the context of Continual Learning, serving as a foundation for further advancements in the field.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 45 of its 72 references that are in the map. First posted on arXiv 21 Dec 2022.

Builds on

45 of 72 references.

References · 45

  1. 2016Overcoming catastrophic forgetting in neural networkscited by 4,718
  2. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  3. 2016Wide Residual Networkscited by 3,057
  4. 2016Learning without Forgettingcited by 2,465
  5. 2015Variational Inference with Normalizing Flowscited by 2,161
  6. 2014Conditional Generative Adversarial Netscited by 2,152
  7. 2017Gradient Episodic Memory for Continual Learningcited by 1,871
  8. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,539
  9. 2017Continual Learning with Deep Generative Replaycited by 1,152
  10. 2018Experience Replay for Continual Learningcited by 1,074
  11. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
  12. 2018Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigencecited by 734
33 more
  1. 2019Three scenarios for continual learningcited by 610
  2. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 591
  3. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  4. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  5. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 320
  6. 2020Sub-Image Anomaly Detection with Deep Pyramid Correspondencescited by 297
  7. 2021Online Continual Learning in Image Classification: An Empirical Surveycited by 242
  8. 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 225
  9. 2019REMIND Your Neural Network to Prevent Catastrophic Forgettingcited by 197
  10. 2018Don't forget, there is more than forgetting: new metrics for Continual Learningcited by 113
  11. 2019Towards Visually Explaining Variational Autoencoderscited by 113
  12. 2020Memory-Efficient Incremental Learning Through Feature Adaptationcited by 107
  13. 2019Latent Replay for Real-Time Continual Learningcited by 97
  14. 2020Rethinking Experience Replay: a Bag of Tricks for Continual Learningcited by 94
  15. 2019A Deep Journey into Super-resolution: A surveycited by 86
  16. 2020Imbalanced Continual Learning with Partitioning Reservoir Samplingcited by 71
  17. 2018Generative Models from the perspective of Continual Learningcited by 69
  18. 2020SpaceNet: Make Free Space For Continual Learningcited by 53
  19. 2017Lifelong Generative Modelingcited by 51
  20. 2018Pseudo-Rehearsal: Achieving Deep Reinforcement Learning without Catastrophic Forgettingcited by 46
  21. 2020Improved anomaly detection by training an autoencoder with skip connections on images corrupted with Stain-shaped noisecited by 39
  22. 2019Efficient Continual Learning in Neural Networks with Embedding Regularizationcited by 20
  23. 2021Regularization-based Continual Learning for Anomaly Detection in Discrete Manufacturingcited by 19
  24. 2021Task-agnostic Continual Learning with Hybrid Probabilistic Modelscited by 16
  25. 2022Practical Recommendations for Replay-based Continual Learning Methodscited by 16
  26. 2020Pseudo-Rehearsal for Continual Learning with Normalizing Flowscited by 10
  27. 2022Relay Hindsight Experience Replay: Self-Guided Continual Reinforcement Learning for Sequential Object Manipulation Tasks with Sparse Rewardscited by 10
  28. 2020Storing Encoded Episodes as Concepts for Continual Learningcited by 8
  29. 2022A Benchmark and Empirical Analysis for Replay Strategies in Continual Learningcited by 8
  30. 2020ARCADe: A Rapid Continual Anomaly Detectorcited by 7
  31. 2021Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting Datacited by 7
  32. 2022Online Continual Learning via the Meta-learning Update with Multi-scale Knowledge Distillation and Data Augmentationcited by 6
  33. 2022A Multi-label Continual Learning Framework to Scale Deep Learning Approaches for Packaging Equipment Monitoringcited by 4

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Cited by year (2026 to 7 Oct)

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

9 papers.

  1. 2024Unveiling the Anomalies in an Ever-Changing World: A Benchmark for Pixel-Level Anomaly Detection in Continual Learningcited by 13
  2. 2024Latent Distillation for Continual Object Detection at the Edgecited by 4
  3. 2025Memory Efficient Continual Learning for Edge-Based Visual Anomaly Detectioncited by 4
  4. 2025Towards Continual Visual Anomaly Detection in the Medical Domaincited by 3
  5. 2025MECAD: A multi-expert architecture for continual anomaly detectioncited by 1
  6. 2025MoViAD: A Modular Library for Visual Anomaly Detectioncited by 0
  7. 2025The Unreasonable Effectiveness of Randomized Representations in Online Continual Graph Learningcited by 0
  8. 2026AdapTS: Lightweight Teacher-Student Approach for Multi-Class and Continual Visual Anomaly Detectioncited by 0
  9. 2026TypiCore: A Hybrid Active Query Strategy for Class-Incremental Learning on Time Seriescited by 0