  Neural networks designed for the task of classification have become a
commodity in recent years. Many works target the development of better
networks, which results in a complexification of their architectures with more
layers, multiple sub-networks, or even the combination of multiple classifiers.
In this paper, we show how to redesign a simple network to reach excellent
performances, which are better than the results reproduced with CapsNet on
several datasets, by replacing a layer with a Hit-or-Miss layer. This layer
contains activated vectors, called capsules, that we train to hit or miss a
central capsule by tailoring a specific centripetal loss function. We also show
how our network, named HitNet, is capable of synthesizing a representative
sample of the images of a given class by including a reconstruction network.
This possibility allows to develop a data augmentation step combining
information from the data space and the feature space, resulting in a hybrid
data augmentation process. In addition, we introduce the possibility for
HitNet, to adopt an alternative to the true target when needed by using the new
concept of ghost capsules, which is used here to detect potentially mislabeled
images in the training data.
