Continuous Kendall Shape Variational Autoencoders

Sharvaree P. Vadgama, Jakub M. Tomczak, Erik J. Bekkers

Abstract: We present an approach for unsupervised learning of geometrically meaningful representations via equivariant variational autoencoders (VAEs) with hyperspherical latent representations. The equivariant encoder/decoder ensures that these latents are geometrically meaningful and grounded in the input space. Mapping these geometry-