**arXiv ID:** 2109.01394 **Authors:** T. Anderson Keller, Max Welling **Published:** 2021-09-03T09:25:57Z **Abstract:** In this work we seek to bridge the concepts of topographic organization and equivariance in neural networks. To accomplish this, we introduce the Topographic VAE: a novel method for efficiently training deep generative models with topographically organized latent variables. We show that such a model indeed learns to organize its activations according to salient characteristi...
Installs into .claude/skills of the current project.
Sponsors
SSkills DirectorySkills Directory
Know which skills are safe — weekly.
SSkills DirectorySkills Directory
Is that skill safe to install?
Are you the author of Topographic Vaes Learn Equivariant Capsules?
Add the live security badge to your README. It updates with every re-scan.
[](https://www.skillsdirectory.com/skills/hiyenwong-topographic-vaes-learn-equivariant-capsules)
# Topographic VAEs learn Equivariant Capsules
**arXiv ID:** 2109.01394
**Authors:** T. Anderson Keller, Max Welling
**Published:** 2021-09-03T09:25:57Z
**Abstract:**
In this work we seek to bridge the concepts of topographic organization and equivariance in neural networks. To accomplish this, we introduce the Topographic VAE: a novel method for efficiently training deep generative models with topographically organized latent variables. We show that such a model indeed learns to organize its activations according to salient characteristics such as digit class, width, and style on MNIST. Furthermore, through topographic organization over time (i.e. temporal coherence), we demonstrate how predefined latent space transformation operators can be encouraged for observed transformed input sequences -- a primitive form of unsupervised learned equivariance. We demonstrate that this model successfully learns sets of approximately equivariant features (i.e. "capsules") directly from sequences and achieves higher likelihood on correspondingly transforming test sequences. Equivariance is verified quantitatively by measuring the approximate commutativity of the inference network and the sequence transformations. Finally, we demonstrate approximate equivariance to complex transformations, expanding upon the capabilities of existing group equivariant neural networks.
## Skill Description
This skill is generated from the arXiv paper: Topographic VAEs learn Equivariant Capsules (2109.01394).
## How to Use
[To be filled in by the user or by future automation]
## References
- [arXiv:2109.01394](http://arxiv.org/abs/2109.01394v2)
