  Few-shot image generation aims to generate data of an unseen category based
on only a few samples. Apart from basic content generation, a bunch of
downstream applications hopefully benefit from this task, such as low-data
detection and few-shot classification. To achieve this goal, the generated
images should guarantee category retention for classification beyond the visual
quality and diversity. In our preliminary work, we present an ``editing-based''
framework Attribute Group Editing (AGE) for reliable few-shot image generation,
which largely improves the generation performance. Nevertheless, AGE's
performance on downstream classification is not as satisfactory as expected.
This paper investigates the class inconsistency problem and proposes Stable
Attribute Group Editing (SAGE) for more stable class-relevant image generation.
SAGE takes use of all given few-shot images and estimates a class center
embedding based on the category-relevant attribute dictionary. Meanwhile,
according to the projection weights on the category-relevant attribute
dictionary, we can select category-irrelevant attributes from the similar seen
categories. Consequently, SAGE injects the whole distribution of the novel
class into StyleGAN's latent space, thus largely remains the category retention
and stability of the generated images. Going one step further, we find that
class inconsistency is a common problem in GAN-generated images for downstream
classification. Even though the generated images look photo-realistic and
requires no category-relevant editing, they are usually of limited help for
downstream classification. We systematically discuss this issue from both the
generative model and classification model perspectives, and propose to boost
the downstream classification performance of SAGE by enhancing the pixel and
frequency components.
