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A Multi-directional Meta-Learning Framework for Class-Generalizable Anomaly Detection

Published: January 27, 2026 | arXiv ID: 2601.19833v1

By: Padmaksha Roy, Lamine Mili, Almuatazbellah Boker

Potential Business Impact:

Finds new problems computers haven't seen before.

Business Areas:
Machine Learning Artificial Intelligence, Data and Analytics, Software

In this paper, we address the problem of class-generalizable anomaly detection, where the objective is to develop a unified model by focusing our learning on the available normal data and a small amount of anomaly data in order to detect the completely unseen anomalies, also referred to as the out-of-distribution (OOD) classes. Adding to this challenge is the fact that the anomaly data is rare and costly to label. To achieve this, we propose a multidirectional meta-learning algorithm -- at the inner level, the model aims to learn the manifold of the normal data (representation); at the outer level, the model is meta-tuned with a few anomaly samples to maximize the softmax confidence margin between the normal and anomaly samples (decision surface calibration), treating normals as in-distribution (ID) and anomalies as out-of-distribution (OOD). By iteratively repeating this process over multiple episodes of predominantly normal and a small number of anomaly samples, we realize a multidirectional meta-learning framework. This two-level optimization, enhanced by multidirectional training, enables stronger generalization to unseen anomaly classes.

Country of Origin
πŸ‡ΊπŸ‡Έ United States

Page Count
11 pages

Category
Computer Science:
Machine Learning (CS)