Holographic-(V)AE: An end-to-end SO(3)-equivariant (variational) autoencoder in Fourier space.

ABSTRACT: Group-equivariant neural networks have emerged as an efficient approach to model complex data, using generalized convolutions that respect the relevant symmetries of a system. These techniques have made advances in both the supervised learning tasks for classification and regression, and the unsupervised tasks to generate new data. However, little work has been done in leveraging the symmetry-aware expressive representations that could be extracted from these approaches. Here, we present holographic-(variational) autoencoder [H-(V)AE], a fully end-to-end SO(3)-equivariant (variational) autoencoder in Fourier space, suitable for unsupervised learning and generation of data distributed around a specified origin in 3D. H-(V)AE is trained to reconstruct the spherical Fourier encoding of

SUBMITTER: Visani GM 

PROVIDER: S-EPMC11661850 | biostudies-literature | 2024 Apr-Jun

REPOSITORIES: biostudies-literature