2 papers
cs.LG2026
OCSVM-Guided Representation Learning for Unsupervised Anomaly Detection
Nicolas Pinon, Robin Trombetta, Carole Lartizien
Unsupervised anomaly detection (UAD) aims to detect anomalies without labeled data, a necessity in many machine learning applications where anomalous samples are rare or not availa…
eess.IV2025
GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models
Daria Zotova, Nicolas Pinon, Robin Trombetta +3
Background and Objective. Research in the cross-modal medical image translation domain has been very productive over the past few years in tackling the scarce availability of large…