CRAS aims to address inter-class interference and intra-class overlap in multi-class anomaly detection through center-aware residual learning and distance-guided anomaly synthesis. This repository ...

Industrial anomaly detection is hindered by data inefficiency and dependence on large-scale training sets. We introduce CLIP-FSQAE, a novel framework for few-shot anomaly detection that integrates ...

Improved injury detection through harmonizing multi-site neuroimaging data after experimental TBI: a Translational Outcomes Project in Neurotrauma consortium study ...

The natural gas pipeline network has a complex topology with variable flow directions, and the supply demand relationships between nodes exhibit cyclical, fluctuating, and time-varying trends.

Overview The dissertation explores the theoretical foundations and practical implications of label blindness in unlabeled out-of-distribution (OOD) detection methods. The main contribution is the ...

Self-Supervised Learning Meets Custom Autoencoder Classifier: A Semi-Supervised Approach for Encrypted Traffic Anomaly Detection ...