2 research outputs found
Inference and Learning for Generative Capsule Models
Capsule networks (see e.g. Hinton et al., 2018) aim to encode knowledge of
and reason about the relationship between an object and its parts. In this
paper we specify a generative model for such data, and derive a variational
algorithm for inferring the transformation of each model object in a scene, and
the assignments of observed parts to the objects. We derive a learning
algorithm for the object models, based on variational expectation maximization
(Jordan et al., 1999). We also study an alternative inference algorithm based
on the RANSAC method of Fischler and Bolles (1981). We apply these inference
methods to (i) data generated from multiple geometric objects like squares and
triangles ("constellations"), and (ii) data from a parts-based model of faces.
Recent work by Kosiorek et al. (2019) has used amortized inference via stacked
capsule autoencoders (SCAEs) to tackle this problem -- our results show that we
significantly outperform them where we can make comparisons (on the
constellations data).Comment: 31 pages, 6 figures. This paper extends our previous work
(arxiv:2103.06676) by covering the learning of the models as well as
inference. Paper accepted for publication in Neural Computatio
Inference and Learning for Generative Capsule Models
Capsule networks (see e.g. Hinton et al., 2018) aim to encode knowledge of
and reason about the relationship between an object and its parts. In this
paper we specify a generative model for such data, and derive a variational
algorithm for inferring the transformation of each model object in a scene, and
the assignments of observed parts to the objects. We derive a learning
algorithm for the object models, based on variational expectation maximization
(Jordan et al., 1999). We also study an alternative inference algorithm based
on the RANSAC method of Fischler and Bolles (1981). We apply these inference
methods to (i) data generated from multiple geometric objects like squares and
triangles ("constellations"), and (ii) data from a parts-based model of faces.
Recent work by Kosiorek et al. (2019) has used amortized inference via stacked
capsule autoencoders (SCAEs) to tackle this problem -- our results show that we
significantly outperform them where we can make comparisons (on the
constellations data).Comment: 31 pages, 6 figures. This paper extends our previous work
(arxiv:2103.06676) by covering the learning of the models as well as
inference. Paper accepted for publication in Neural Computatio