An Effective Hit-or-Miss Layer Favoring Feature Interpretation as Learned Prototypes Deformations

Understand

Neural networks designed for the task of classification have become a commodity in recent years.

• Many works target the development of more effective networks, which results in a complexification of their architectures with more layers, multiple sub-networks, or even the combination of multiple classifiers, but this often comes at the expense of producing uninterpretable black boxes.
• In this paper, we redesign a simple capsule network to enable it to synthesize class-representative samples, called prototypes, by replacing the last layer with a novel Hit-or-Miss layer.
• This layer contains activated vectors, called capsules, that we train to hit or miss a fixed target capsule by tailoring a specific centripetal loss function.