  Anomaly detection has many important applications, such as monitoring
industrial equipment. Despite recent advances in anomaly detection with
deep-learning methods, it is unclear how existing solutions would perform under
out-of-distribution scenarios, e.g., due to shifts in machine load or
environmental noise. Grounded in the application of machine health monitoring,
we propose a framework that adapts to new conditions with few-shot samples.
Building upon prior work, we adopt a classification-based approach for anomaly
detection and show its equivalence to mixture density estimation of the normal
samples. We incorporate an episodic training procedure to match the few-shot
setting during inference. We define multiple auxiliary classification tasks
based on meta-information and leverage gradient-based meta-learning to improve
generalization to different shifts. We evaluate our proposed method on a
recently-released dataset of audio measurements from different machine types.
It improved upon two baselines by around 10% and is on par with best-performing
model reported on the dataset.
