  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.
