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• W3134113626 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." @default.
• W3134113626 created "2021-03-15" @default.
• W3134113626 creator A5030880294 @default.
• W3134113626 creator A5034538103 @default.
• W3134113626 date "2021-02-28" @default.
• W3134113626 modified "2023-09-23" @default.
• W3134113626 title "Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection" @default.
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• W3134113626 doi "https://doi.org/10.48550/arxiv.2103.00684" @default.
• W3134113626 hasPublicationYear "2021" @default.
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