  Prototypical part network (ProtoPNet) methods have been designed to achieve
interpretable classification by associating predictions with a set of training
prototypes, which we refer to as trivial prototypes because they are trained to
lie far from the classification boundary in the feature space. Note that it is
possible to make an analogy between ProtoPNet and support vector machine (SVM)
given that the classification from both methods relies on computing similarity
with a set of training points (i.e., trivial prototypes in ProtoPNet, and
support vectors in SVM). However, while trivial prototypes are located far from
the classification boundary, support vectors are located close to this
boundary, and we argue that this discrepancy with the well-established SVM
theory can result in ProtoPNet models with inferior classification accuracy. In
this paper, we aim to improve the classification of ProtoPNet with a new method
to learn support prototypes that lie near the classification boundary in the
feature space, as suggested by the SVM theory. In addition, we target the
improvement of classification results with a new model, named ST-ProtoPNet,
which exploits our support prototypes and the trivial prototypes to provide
more effective classification. Experimental results on CUB-200-2011, Stanford
Cars, and Stanford Dogs datasets demonstrate that ST-ProtoPNet achieves
state-of-the-art classification accuracy and interpretability results. We also
show that the proposed support prototypes tend to be better localised in the
object of interest rather than in the background region.
