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

• URL: http://arxiv.org/abs/2601.19833v1
• Date: Tue, 27 Jan 2026 17:39:11 GMT
• Title: A Multi-directional Meta-Learning Framework for Class-Generalizable Anomaly Detection
• Authors: Padmaksha Roy, Lamine Mili, Almuatazbellah Boker,
• Abstract summary: We propose a multidirectional meta-learning algorithm to learn the manifold of the normal data.<n>At the inner level, the model aims to learn the manifold of the normal data.<n>At the outer level, the model is meta-tuned with a few anomaly samples.
• Score: 2.893006778402251
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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