Meta-learning with GANs for anomaly detection, with deployment in high-speed rail inspection system. (arXiv:2202.05795v1 [cs.LG])

Anomaly detection has been an active research area with a wide range of
potential applications. Key challenges for anomaly detection in the AI era with
big data include lack of prior knowledge of potential anomaly types, highly
complex and noisy background in input data, scarce abnormal samples, and
imbalanced training dataset. In this work, we propose a meta-learning framework
for anomaly detection to deal with these issues. Within this framework, we
incorporate the idea of generative adversarial networks (GANs) with …

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