Machine Heart

May 20, 2026 · Artificial Intelligence

Can Tabular Anomaly Detection Move Beyond One‑for‑One? OFA‑TAD Introduces a One‑for‑All Paradigm

Tabular anomaly detection traditionally requires training a separate model for each dataset (one‑for‑one), but the new OFA‑TAD framework trains once on multiple source tables and directly transfers to unseen target tables without fine‑tuning, leveraging multi‑view distance encoding, MoE fusion, and synthetic pseudo‑anomalies to achieve state‑of‑the‑art performance across 34 datasets in 14 domains.