Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection

TL;DR

This paper introduces a meta-learning approach for supervised anomaly detection that leverages eigenvalue solvers to enable quick adaptation to new tasks with limited data, outperforming existing methods.

Contribution

It presents a novel meta-learning framework that formulates adaptation as a differentiable eigenvalue problem for one-class classification, improving anomaly detection on unseen tasks.

Findings

Achieves better performance than existing methods on various datasets.

Enables rapid adaptation to new tasks with few labeled instances.

Uses a differentiable eigenvalue solver for effective meta-training.

Abstract

Neural network-based anomaly detection methods have shown to achieve high performance. However, they require a large amount of training data for each task. We propose a neural network-based meta-learning method for supervised anomaly detection. The proposed method improves the anomaly detection performance on unseen tasks, which contains a few labeled normal and anomalous instances, by meta-training with various datasets. With a meta-learning framework, quick adaptation to each task and its effective backpropagation are important since the model is trained by the adaptation for each epoch. Our model enables them by formulating adaptation as a generalized eigenvalue problem with one-class classification; its global optimum solution is obtained, and the solver is differentiable. We experimentally demonstrate that the proposed method achieves better performance than existing anomaly detection and few-shot learning methods on various datasets.