• Corpus ID: 236772195

Explainable Deep Few-shot Anomaly Detection with Deviation Networks

@article{Pang2021ExplainableDF,
  title={Explainable Deep Few-shot Anomaly Detection with Deviation Networks},
  author={Guansong Pang and Choubo Ding and Chunhua Shen and Anton van den Hengel},
  journal={ArXiv},
  year={2021},
  volume={abs/2108.00462}
}
Existing anomaly detection paradigms overwhelmingly focus on training detection models using exclusively normal data or unlabeled data (mostly normal samples), assuming no access to any labeled anomaly data. One notorious issue with these approaches is that they are weak in discriminating anomalies from normal samples due to the lack of the knowledge about the anomalies. Here, we study the problem of few-shot anomaly detection, in which we aim at using a few labeled anomaly examples to train… 

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