  Few-shot image generation is a challenging task since it aims to generate
diverse new images for an unseen category with only a few images. Existing
methods suffer from the trade-off between the quality and diversity of
generated images. To tackle this problem, we propose Hyperbolic Attribute
Editing~(HAE), a simple yet effective method. Unlike other methods that work in
Euclidean space, HAE captures the hierarchy among images using data from seen
categories in hyperbolic space. Given a well-trained HAE, images of unseen
categories can be generated by moving the latent code of a given image toward
any meaningful directions in the Poincar\'e disk with a fixing radius. Most
importantly, the hyperbolic space allows us to control the semantic diversity
of the generated images by setting different radii in the disk. Extensive
experiments and visualizations demonstrate that HAE is capable of not only
generating images with promising quality and diversity using limited data but
achieving a highly controllable and interpretable editing process.
