An optimization method for out-of-distribution anomaly detection models

2023/02/02 by Ji Qiu, Hongmei Shi, Qiu, Ji +5
Computer Science · Engineering · #Advanced Chemical Sensor Technologies #Anomaly Detection Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Fault Detection and Control Systems

paper · pdf · doi:10.48550/arxiv.2302.00939

openalex publication_date 2023/02/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Frequent false alarms impede the promotion of unsupervised anomaly detection algorithms in industrial applications. Potential characteristics of false alarms depending on the trained detector are revealed by investigating density probability distributions of prediction scores in the out-of-distribution anomaly detection tasks. An SVM-based classifier is exploited as a post-processing module to identify false alarms from the anomaly map at the object level. Besides, a sample synthesis strategy is devised to incorporate fuzzy prior knowledge on the specific application in the anomaly-free training dataset. Experimental results illustrate that the proposed method comprehensively improves the performances of two segmentation models at both image and pixel levels on two industrial applications.

Related

• Robust Anomaly Map Assisted Multiple Defect Detection with Supervised Classification Techniques
• Anomaly Detection Under Uncertainty Using Distributionally Robust Optimization Approach
• Modern Anomaly Detection Methods in Industry: A Comparative Analysis of Machine Learning Algorithms and Their Application to Improve the Efficiency of Manufacturing Processes
• Learning Competitive and Discriminative Reconstructions for Anomaly Detection
• Leveraging Learning Bias for Noisy Anomaly Detection