Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection
This work addresses the need for efficient anomaly detection in scenarios with limited labeled data, though it is incremental as it builds on meta-learning and one-class classification techniques.
The paper tackles the problem of supervised anomaly detection requiring large training data per task by proposing a neural network-based meta-learning method that adapts quickly to new tasks with few labeled instances, achieving better performance than existing methods on various datasets.
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.