3 papers
cs.CV2026
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…
cs.CV2026
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…
cs.CV2025
Background-Aware Defect Generation for Robust Industrial Anomaly Detection
Youngjae Cho, Gwangyeol Kim, Sirojbek Safarov +2
Detecting anomalies in industrial settings is challenging due to the scarcity of labeled anomalous data. Generative models can mitigate this issue by synthesizing realistic defect…