Learning Weighted Submanifolds With Riemannian Variational Autoencoders

Miolane, N., Holmes, S.

CVPR Conference of Computer Vision and Pattern Recognition.

Manifold-valued data naturally arises in medical imaging. One of the challenges that naturally arises consists of finding a lower-dimensional subspace for representing such manifold-valued data. Traditional techniques, like principal component analysis, are ill-adapted to tackle non-Euclidean spaces. We introduce Riemannian Variational Autoencoders to perform weighted submanifold learning powered by amortized variational inference.