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cs.LG2026
An Insight on Evaluation Metrics Under the Imbalanced Case of Anomaly Detection
Romain Hermary, Nesryne Mejri, Djamila Aouada
Anomaly detection is inherently characterised by severe class imbalance, making the interpretation of evaluation metrics challenging. Although metrics such as AUROC, AUPR, F1-score…
cs.LG2026
ASTER: Latent Pseudo-Anomaly Generation for Unsupervised Time-Series Anomaly Detection
Romain Hermary, Samet Hicsonmez, Dan Pineau +2
Time-series anomaly detection (TSAD) is critical in domains such as industrial monitoring, healthcare, and cybersecurity, but it remains challenging due to rare and heterogeneous a…
cs.LG2025
When Unsupervised Domain Adaptation meets One-class Anomaly Detection: Addressing the Two-fold Unsupervised Curse by Leveraging Anomaly Scarcity
Nesryne Mejri, Enjie Ghorbel, Anis Kacem +3
This paper introduces the first fully unsupervised domain adaptation (UDA) framework for unsupervised anomaly detection (UAD). The performance of UAD techniques degrades significan…