# On-Device Continual Learning for Unsupervised Visual Anomaly Detection in Dynamic Manufacturing

arXiv: https://arxiv.org/abs/2512.13497 · HTML: https://mlsanity.com/paper/2512.13497/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 26 references that are in the map.
- First posted on arXiv 15 Dec 2025.

## Builds on (most cited first, 16 of 16)

- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Towards Total Recall in Industrial Anomaly Detection](https://mlsanity.com/paper/2106.08265/) (2021)
- [DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection](https://mlsanity.com/paper/2108.07610/) (2021)
- [SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation](https://mlsanity.com/paper/2207.14315/) (2022)
- [EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies](https://mlsanity.com/paper/2303.14535/) (2023)
- [IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing](https://mlsanity.com/paper/2301.13359/) (2023)
- [PaSTe: Improving the Efficiency of Visual Anomaly Detection at the Edge](https://mlsanity.com/paper/2410.11591/) (2024)
- [TinyML: Tools, Applications, Challenges, and Future Research Directions](https://mlsanity.com/paper/2303.13569/) (2023)
- [Unveiling the Anomalies in an Ever-Changing World: A Benchmark for Pixel-Level Anomaly Detection in Continual Learning](https://mlsanity.com/paper/2403.15463/) (2024)
- [Benchmarking Unsupervised Anomaly Detection and Localization](https://mlsanity.com/paper/2205.14852/) (2022)
- [RAMAN: A Re-configurable and Sparse tinyML Accelerator for Inference on Edge](https://mlsanity.com/paper/2306.06493/) (2023)
- [A Continual Learning Framework for Adaptive Defect Classification and Inspection](https://mlsanity.com/paper/2203.08796/) (2022)
- [Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review](https://mlsanity.com/paper/2501.11310/) (2025)
- [On-device Online Learning and Semantic Management of TinyML Systems](https://mlsanity.com/paper/2405.07601/) (2024)
- [Towards Continual Visual Anomaly Detection in the Medical Domain](https://mlsanity.com/paper/2508.18013/) (2025)
- [Leveraging Unsupervised Learning for Cost-Effective Visual Anomaly Detection](https://mlsanity.com/paper/2409.15980/) (2024)

## Abstract (arXiv)

In modern manufacturing, Visual Anomaly Detection (VAD) is essential for automated inspection and consistent product quality. Yet, increasingly dynamic and flexible production environments introduce key challenges: First, frequent product changes in small-batch and on-demand manufacturing require rapid model updates. Second, legacy edge hardware lacks the resources to train and run large AI models. Finally, both anomalous and normal training data are often scarce, particularly for newly introduced product variations. We investigate on-device continual learning for unsupervised VAD with localization, extending the PatchCore to incorporate online learning for real-world industrial scenarios. The proposed method leverages a lightweight feature extractor and an incremental coreset update mechanism based on k-center selection, enabling rapid, memory-efficient adaptation from limited data while eliminating costly cloud retraining. Evaluations on an industrial use case are conducted using a testbed designed to emulate flexible production with frequent variant changes in a controlled environment. Our method achieves a 12% AUROC improvement over the baseline, an 80% reduction in memory usage, and faster training compared to batch retraining. These results confirm that our method delivers accurate, resource-efficient, and adaptive VAD suitable for dynamic and smart manufacturing.