Capsule Networks -- A Probabilistic Perspective
Lewis Smith, Lisa Schut, Yarin Gal, Mark van der Wilk

TL;DR
This paper presents a probabilistic generative model for capsule networks that explicitly encodes object pose relationships, aiming to improve robustness to viewpoint changes and providing insights into inference failures.
Contribution
It introduces a probabilistic perspective on capsule networks, separating generative modeling from inference, and explores test-time optimization to address inference challenges.
Findings
The probabilistic model clarifies capsule assumptions and their limitations.
Test-time optimization improves inference quality.
The unified objective demonstrates practical applicability.
Abstract
'Capsule' models try to explicitly represent the poses of objects, enforcing a linear relationship between an object's pose and that of its constituent parts. This modelling assumption should lead to robustness to viewpoint changes since the sub-object/super-object relationships are invariant to the poses of the object. We describe a probabilistic generative model which encodes such capsule assumptions, clearly separating the generative parts of the model from the inference mechanisms. With a variational bound we explore the properties of the generative model independently of the approximate inference scheme, and gain insights into failures of the capsule assumptions and inference amortisation. We experimentally demonstrate the applicability of our unified objective, and demonstrate the use of test time optimisation to solve problems inherent to amortised inference in our model.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Gaussian Processes and Bayesian Inference · Explainable Artificial Intelligence (XAI)
