Methods › Computer Vision › Generative Models › Topographic VAE
Topographic VAE
Introduced by T. Anderson Keller et al. in Topographic VAEs learn Equivariant Capsules
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Topographic VAE is a method for efficiently training deep generative models with topographically organized latent variables. The model learns sets of approximately equivariant features (i.e. "capsules") directly from sequences and achieves higher likelihood on correspondingly transforming test sequences. The combined color/rotation transformation in input space τ_g becomes encoded as a Roll within the capsule dimension. The model is thus able decode unseen sequence elements by encoding a partial sequence and Rolling activations within the capsules. This resembles a commutative diagram.
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Topographic VAEs learn Equivariant Capsules 3 Sep 2021 · 1 repository · arXiv:2109.01394
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