  Few-shot image generation is a challenging task even using the
state-of-the-art Generative Adversarial Networks (GANs). Due to the unstable
GAN training process and the limited training data, the generated images are
often of low quality and low diversity. In this work, we propose a new
editing-based method, i.e., Attribute Group Editing (AGE), for few-shot image
generation. The basic assumption is that any image is a collection of
attributes and the editing direction for a specific attribute is shared across
all categories. AGE examines the internal representation learned in GANs and
identifies semantically meaningful directions. Specifically, the class
embedding, i.e., the mean vector of the latent codes from a specific category,
is used to represent the category-relevant attributes, and the
category-irrelevant attributes are learned globally by Sparse Dictionary
Learning on the difference between the sample embedding and the class
embedding. Given a GAN well trained on seen categories, diverse images of
unseen categories can be synthesized through editing category-irrelevant
attributes while keeping category-relevant attributes unchanged. Without
re-training the GAN, AGE is capable of not only producing more realistic and
diverse images for downstream visual applications with limited data but
achieving controllable image editing with interpretable category-irrelevant
directions.
