5 papers
Memory-Bounded Continuation of Greedy Sampling for Continual Anomaly Detection
Yoon Gyo Jung, Jaewoo Park, Kuan-Chuan Peng +2
Greedy sampling produces a compact yet representative summary of normal data, which is essential for reliable anomaly detection that relies on measuring distance from normality. Fo…
Memory-Distilled Selection for Noise-Robust Anomaly Detection
Sirojbek Safarov, Jaewoo Park, Yoon Gyo Jung +4
Anomaly detection (AD) under data contamination is critical for deploying unsupervised defect detection in industrial environments, where curating perfectly clean training sets is…
TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection
Yoon Gyo Jung, Jaewoo Park, Jaeho Yoon +4
We aim to solve unsupervised anomaly detection in a practical challenging environment where the normal dataset is both contaminated with defective regions and its product class dis…
Joint Training of Image Generator and Detector for Road Defect Detection
Kuan-Chuan Peng
Road defect detection is important for road authorities to reduce the vehicle damage caused by road defects. Considering the practical scenarios where the defect detectors are typi…
Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts
Chiao-An Yang, Kuan-Chuan Peng, Raymond A. Yeh
Anomaly detection (AD) identifies the defect regions of a given image. Recent works have studied AD, focusing on learning AD without abnormal images, with long-tailed distributed t…