Disentangled latent representations of images with atomic autoencoders

Alasdair Newson (1, 2, 3) , Yann Traonmilin (4)

1 LTCI - Laboratoire Traitement et Communication de l'Information
2 IDS - Département Images, Données, Signal
3 IMAGES - Image, Modélisation, Analyse, GEométrie, Synthèse
4 IMB - Institut de Mathématiques de Bordeaux

We present the atomic autoencoder architecture, which decomposes an image as the sum of elementary parts that are parametrized by simple separate blocks of latent codes. We show that this simple architecture is induced by the denition of a general low-dimensional model of the considered data. We also highlight the fact that the atomic autoencoder achieves disentangled low-dimensional representations under minimal hypotheses. Experiments show that their implementation with deep neural networks is successful at learning disentangled representations on two dierent examples: images constructed with simple parametric curves and images of ltered o-the-grid spikes.

Keywords: low dimensional models autoencoders disentanglement; low dimensional models; autoencoders; disentanglement

Domains: Mathematics [math]; Computer Science [cs]; Statistics [stat]

Alasdair Newson, Yann Traonmilin. Disentangled latent representations of images with atomic autoencoders. 2023. ⟨hal-03962759v1⟩