<ul data-eligibleForWebStory="false"><li>Outlier detection (OD) is essential in distinguishing inliers and outliers in unlabeled datasets across various domains but often requires dataset-specific tuning and model training.</li><li>UniOD is introduced as a universal OD framework that uses labeled datasets to create a single model capable of detecting outliers in diverse domains.</li><li>UniOD transforms datasets into graphs, maintains consistent node features, and treats outlier detection as a node-classification task, enabling generalization to new domains.</li><li>Evaluation of UniOD on 15 benchmark OD datasets against 15 state-of-the-art approaches showcases its effectiveness in avoiding model tuning, reducing computational costs, and improving accuracy in real-world applications.</li></ul>

UniOD: A Universal Model for Outlier Detection across Diverse Domains

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