Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

3-2025

Abstract

Visual anomaly detection targets to detect images that notably differ from normal pattern, and it has found extensive application in identifying defective parts within the manufacturing industry. These anomaly detection paradigms predominantly focus on training detection models using only clean, unlabeled normal samples, assuming an absence of contamination; a condition often unmet in real-world scenarios. The performance of these methods significantly depends on the quality of the data and usually decreases when exposed to noise. We introduce a systematic adaptive method that employs deviation learning to compute anomaly scores end-to-end while addressing data contamination by assigning relative importance to the weights of individual instances. In this approach, the anomaly scores for normal instances are designed to approximate scalar scores obtained from the known prior distribution. Meanwhile, anomaly scores for anomaly examples are adjusted to exhibit statistically significant deviations from these reference scores. Our approach incorporates a constrained optimization problem within the deviation learning framework to update instance weights, resolving this problem for each mini-batch. Comprehensive experiments on the MVTec and VisA benchmark datasets indicate that our proposed method surpasses competing techniques and exhibits both stability and robustness in the presence of data contamination. Our source code is available at https://github.com/anindyasdas/ADL4VAD/

Keywords

adaptive deviation learning, anomaly detection, deviation networks, robust detection, visual anomalies

Discipline

Artificial Intelligence and Robotics | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV): Tucson, AZ, February 26 - March 4: Proceedings

First Page

8863

Last Page

8872

ISBN

9798331510831

Identifier

10.1109/WACV61041.2025.00859

Publisher

IEEE

City or Country

Pistacataway

Additional URL

https://doi.org/10.1109/WACV61041.2025.00859

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