Mixture of Gaussian-distributed Prototypes with Generative Modelling for
  Interpretable Image Classification
Chong Wang Yuanhong Chen Fengbei Liu Davis James McCarthy Helen Frazer Gustavo Carneiro
Prototypical-part interpretable methods, e.g., ProtoPNet, enhance
interpretability by connecting classification predictions to class-specific
training prototypes, thereby offering an intuitive insight into their
decision-making. Current methods rely on a discriminative classifier trained
with point-based learning techniques that provide specific values for
prototypes. Such prototypes have relatively low representation power due to
their sparsity and potential redundancy, with each prototype containing no
variability measure. In this paper, we present a new generative learning of
prototype distributions, named Mixture of Gaussian-distributed Prototypes
(MGProto), which are represented by Gaussian mixture models (GMM). Such an
approach enables the learning of more powerful prototype representations since
each learned prototype will own a measure of variability, which naturally
reduces the sparsity given the spread of the distribution around each
prototype, and we also integrate a prototype diversity objective function into
the GMM optimisation to reduce redundancy. Incidentally, the generative nature
of MGProto offers a new and effective way for detecting out-of-distribution
samples. To improve the compactness of MGProto, we further propose to prune
Gaussian-distributed prototypes with a low prior. Experiments on CUB-200-2011,
Stanford Cars, Stanford Dogs, and Oxford-IIIT Pets datasets show that MGProto
achieves state-of-the-art classification and OoD detection performances with
encouraging interpretability results.