mlsanityData to 14 Oct 2026

MECAD: A multi-expert architecture for continual anomaly detection

arXiv 2512.15323 ↗First posted 17 Dec 2025cs.CVcited by 1

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Abstract (arXiv)

In this paper we propose MECAD, a novel approach for continual anomaly detection using a multi-expert architecture. Our system dynamically assigns experts to object classes based on feature similarity and employs efficient memory management to preserve the knowledge of previously seen classes. By leveraging an optimized coreset selection and a specialized replay buffer mechanism, we enable incremental learning without requiring full model retraining. Our experimental evaluation on the MVTec AD dataset demonstrates that the optimal 5-expert configuration achieves an average AUROC of 0.8259 across 15 diverse object categories while significantly reducing knowledge degradation compared to single-expert approaches. This framework balances computational efficiency, specialized knowledge retention, and adaptability, making it well-suited for industrial environments with evolving product types.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 24 of its 31 references that are in the map. First posted on arXiv 17 Dec 2025.

Builds on

24 of 31 references.

References · 24

  1. 2016Overcoming catastrophic forgetting in neural networkscited by 4,787
  2. 2016iCaRL: Incremental Classifier and Representation Learningcited by 2,333
  3. 2017Memory Aware Synapses: Learning what (not) to forgetcited by 1,105
  4. 2018Experience Replay for Continual Learningcited by 1,095
  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. 2019Gradient based sample selection for online continual learningcited by 571
  8. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 421
  9. 2019Online Continual Learning with Maximally Interfered Retrievalcited by 379
  10. 2016Expert Gate: Lifelong Learning with a Network of Expertscited by 372
  11. 2017Variational Continual Learningcited by 339
  12. 2021Online Coreset Selection for Rehearsal-based Continual Learningcited by 117
12 more
  1. 2024Theory on Mixture-of-Experts in Continual Learningcited by 54
  2. 2024Mixture of Experts Meets Prompt-Based Continual Learningcited by 43
  3. 2024Divide and not forget: Ensemble of selectively trained experts in Continual Learningcited by 38
  4. 2024Unsupervised Continual Anomaly Detection with Contrastively-learned Promptcited by 31
  5. 2023An Incremental Unified Framework for Small Defect Inspectioncited by 25
  6. 2024Continuous Memory Representation for Anomaly Detectioncited by 16
  7. 2022ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labelscited by 15
  8. 2024Unveiling the Anomalies in an Ever-Changing World: A Benchmark for Pixel-Level Anomaly Detection in Continual Learningcited by 13
  9. 2022Continual Learning Approaches for Anomaly Detectioncited by 9
  10. 2021Mixture-of-Variational-Experts for Continual Learningcited by 8
  11. 2022TAME: Task Agnostic Continual Learning using Multiple Expertscited by 8
  12. 2023Lifelong Continual Learning for Anomaly Detection: New Challenges, Perspectives, and Insightscited by 8

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Citing papers per year: 2026: 1.

1 paper.

  1. 2026Training-Free Human-in-the-Loop Anomaly Detection via Memory Bank Correctioncited by 0