  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 appropriate
choices of loss functions including structural similarity index measure (SSIM).
Experiments with limited labeled data for high-speed rail inspection
demonstrate that our meta-learning framework is sharp and robust in identifying
anomalies. Our framework has been deployed in five high-speed railways of China
since 2021: it has reduced more than 99.7% workload and saved 96.7% inspection
time.
