arxiv1d ago

Interpretable Anomaly and Drift Detection with Gaussian Mixture Models

arXiv:2607.16811v2 Announce Type: replace Abstract: We revisit Gaussian Mixture Models (GMMs) as a lightweight, interpretable tool for anomaly detection and, in particular, for detecting distributional drift in data streams. We make three practical choices explicit and evaluate them on seven public

GAISLO7 models · +4 #anomaly detection #machine learning

arxivApr 16

Unsupervised Anomaly Detection in Process-Complex Industrial Time Series: A Real-World Case Study

arXiv:2604.13928v1 Announce Type: new Abstract: Industrial time-series data from real production environments exhibits substantially higher complexity than commonly used benchmark datasets, primarily due to heterogeneous, multi-stage operational processes. As a result, anomaly detection methods vali

ISTERE4 models · +1 #anomaly detection #industrial time-series #autoencoders Read on arxiv →

arxivApr 16bullish

IAD-Unify: A Region-Grounded Unified Model for Industrial Anomaly Segmentation, Understanding, and Generation

arXiv:2604.12440v1 Announce Type: cross Abstract: Real-world industrial inspection requires not only localizing defects, but also explaining them in natural language and generating controlled defect edits. However, existing approaches fail to jointly support all three capabilities within a unified f

DIQW2 models #computer vision #anomaly detection #multitask learning Read on arxiv →