  Computer vision tasks such as image classification, image retrieval and
few-shot learning are currently dominated by Euclidean and spherical
embeddings, so that the final decisions about class belongings or the degree of
similarity are made using linear hyperplanes, Euclidean distances, or spherical
geodesic distances (cosine similarity). In this work, we demonstrate that in
many practical scenarios hyperbolic embeddings provide a better alternative.
