  In open set recognition, a classifier has to detect unknown classes that are
not known at training time. In order to recognize new classes, the classifier
has to project the input samples of known classes in very compact and separated
regions of the features space in order to discriminate outlier samples of
unknown classes. Recently proposed Capsule Networks have shown to outperform
alternatives in many fields, particularly in image recognition, however they
have not been fully applied yet to open-set recognition. In capsule networks,
scalar neurons are replaced by capsule vectors or matrices, whose entries
represent different properties of objects. In our proposal, during training,
capsules features of the same known class are encouraged to match a pre-defined
gaussian, one for each class. To this end, we use the variational autoencoder
framework, with a set of gaussian prior as the approximation for the posterior
distribution. In this way, we are able to control the compactness of the
features of the same class around the center of the gaussians, thus controlling
the ability of the classifier in detecting samples from unknown classes. We
conducted several experiments and ablation of our model, obtaining state of the
art results on different datasets in the open set recognition and unknown
detection tasks.
