From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection
Mike Szklarzewski, CJ George, Gavin Smithson, Christopher Stokes, Dakota Fulp, William M. Jones, Benjamin Wynn, Alexander Ur, Agit Yesiloz, Clint Kallenbach, Mark Swartz, Nathan DeBardeleben, Sharmistha Chakrabarti
cs.LG, cs.CV
Submitted: 2026-08-07
Updated: 2026-08-11
Comments: 8 pages, 8 figures, 6 tables. Accepted as a regular paper at the 25th International Conference on Machine Learning and Applications (ICMLA 2026)
Code: https://github.com/lanl/annomate-microsentryai-workflow
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- What makes a good data augmentation for few-shot unsupervised image anomaly detection?
- IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing
- SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection
- FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows
- Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection
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