  We propose ProtoArgNet, a novel interpretable deep neural architecture for
image classification in the spirit of prototypical-part-learning as found, e.g.
in ProtoPNet. While earlier approaches associate every class with multiple
prototypical-parts, ProtoArgNet uses super-prototypes that combine
prototypical-parts into single prototypical class representations. Furthermore,
while earlier approaches use interpretable classification layers, e.g. logistic
regression in ProtoPNet, ProtoArgNet improves accuracy with multi-layer
perceptrons while relying upon an interpretable reading thereof based on a form
of argumentation. ProtoArgNet is customisable to user cognitive requirements by
a process of sparsification of the multi-layer perceptron/argumentation
component. Also, as opposed to other prototypical-part-learning approaches,
ProtoArgNet can recognise spatial relations between different
prototypical-parts that are from different regions in images, similar to how
CNNs capture relations between patterns recognized in earlier layers.
