Capsule Network Explainability deals with methods to make the internal representations and routing mechanisms of capsule networks transparent.
Capsule networks use groups of neurons, so-called capsules, which encode not only feature activations but also pose information (e.g., position, orientation).
Explainability includes visualizing the activations of individual capsules as well as analyzing the dynamic routing process that determines how information is passed between capsules.
These insights help to understand the model's behavior and improve the models.