  In this paper, we develop and explore deep anomaly detection techniques based
on the capsule network (CapsNet) for image data. Being able to encoding
intrinsic spatial relationship between parts and a whole, CapsNet has been
applied as both a classifier and deep autoencoder. This inspires us to design a
prediction-probability-based and a reconstruction-error-based normality score
functions for evaluating the "outlierness" of unseen images. Our results on
three datasets demonstrate that the prediction-probability-based method
performs consistently well, while the reconstruction-error-based approach is
relatively sensitive to the similarity between labeled and unlabeled images.
Furthermore, both of the CapsNet-based methods outperform the principled
benchmark methods in many cases.
