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.CV2024
Adversarial Feature Alignment: Balancing Robustness and Accuracy in Deep Learning via Adversarial Training
Leo Hyun Park, Jaeuk Kim, Myung Gyo Oh +2
Deep learning models continue to advance in accuracy, yet they remain vulnerable to adversarial attacks, which often lead to the misclassification of adversarial examples. Adversar…