Characteristic discriminative prototype network with detailed interpretation for classification

Existing prototype learning methods provide limited interpretation on which patches from input images are similar to the corresponding prototypes. Moreover, these methods do not consider the diversities among the prototypes, which leads to low classification accuracy. To address these problems, this paper proposes Characteristic Prototype Network (CDPNet) with clear interpretation of local regions and characteristic. The network designs the feature prototype to represent the discriminative feature and the characteristic prototype to characterize the prototype's properties among different individuals. In addition, two novel strategies, dynamic region learning and similarity score minimization among similar intra-class prototypes, are designed to learn the prototypes so as to improve their diversity. Therefore, CDPNet can explain which kind of characteristic within the image is the most important one for classification tasks. The experimental results on wellknown datasets show that CDPNet can provide clearer interpretations and obtain state-of-the-art classification performance in prototype learning.