Mixture of Gaussian-distributed Prototypes with Generative Modelling for Interpretable Image Classification

no code implementations • 30 Nov 2023 • Chong Wang, Yuanhong Chen, Fengbei Liu, Davis James McCarthy, Helen Frazer, Gustavo Carneiro

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.

Decision Making Image Classification