4 papers
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…
When AUC Misleads: Polarization-Aware Evaluation of Deepfake Detectors under Domain Shift
Dat Nguyen, Cosmin Radoi, Romain Hermary +4
Recent advances in generative AI, such as diffusion models and face-swapping tools, have enabled the creation of highly realistic deepfakes, leading to real-world harms including f…
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…
DAVINCI: A Single-Stage Architecture for Constrained CAD Sketch Inference
Ahmet Serdar Karadeniz, Dimitrios Mallis, Nesryne Mejri +3
This work presents DAVINCI, a unified architecture for single-stage Computer-Aided Design (CAD) sketch parameterization and constraint inference directly from raster sketch images.…