Dominik Sturm

Spatially Informed Autoencoders for Interpretable Visual Representation Learning

We introduce spatially informed variational autoencoders (SI-VAE) as self-supervised deep-learning models that use stochastic point processes to predict spatial organization patterns from images. Existing approaches to learning visual representations based on variational autoencoders (VAE) struggle…