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Anomaly detection has many important applications, such as monitoringindustrial equipment. Despite recent advances in anomaly detection withdeep-learning methods, it is unclear how existing solutions would perform underout-of-distribution scenarios, e.g., due to shifts in machine load orenvironmental 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 anomalydetection and show its equivalence to mixture density estimation of the normalsamples. We incorporate an episodic training procedure to match the few-shotsetting during inference. We define multiple auxiliary classification tasksbased on meta-information and leverage gradient-based meta-learning to improvegeneralization to different shifts. We evaluate our proposed method on arecently-released dataset of audio measurements from different machine types.It improved upon two baselines by around 10% and is on par with best-performingmodel reported on the dataset.

Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detection