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Few-shot image generation is a challenging task since it aims to generatediverse new images for an unseen category with only a few images. Existingmethods suffer from the trade-off between the quality and diversity ofgenerated images. To tackle this problem, we propose Hyperbolic AttributeEditing~(HAE), a simple yet effective method. Unlike other methods that work inEuclidean space, HAE captures the hierarchy among images using data from seencategories in hyperbolic space. Given a well-trained HAE, images of unseencategories can be generated by moving the latent code of a given image towardany meaningful directions in the Poincar\'e disk with a fixing radius. Mostimportantly, the hyperbolic space allows us to control the semantic diversityof the generated images by setting different radii in the disk. Extensiveexperiments and visualizations demonstrate that HAE is capable of not onlygenerating images with promising quality and diversity using limited data butachieving a highly controllable and interpretable editing process.

The Euclidean Space is Evil: Hyperbolic Attribute Editing for Few-shot Image Generation