Object-Oriented Learning (OOL): Perception, Representation, and Reasoning
International Conference on Machine Learning (ICML)
Friday July 17, 2020, Virtual Workshop
The video will become available after 1st August 2020 in accordance with the ICML2020 Code of Conduct.
`Capsule' models try to explicitly represent the poses of objects, enforcing a linear relationship between an objects pose and those of its constituent parts. This modelling assumption should lead to robustness to viewpoint changes since the object-component relationships are invariant to the poses of the object. We describe a probabilistic generative model that encodes these assumptions. Our probabilistic formulation separates the generative assumptions of the model from the inference scheme, which we derive from a variational bound. We experimentally demonstrate the applicability of our unified objective, and the use of test time optimisation to solve problems inherent to amortised inference.