  Due to the absence of fine structure and texture information, existing
fusion-based few-shot image generation methods suffer from unsatisfactory
generation quality and diversity. To address this problem, we propose a novel
feature Equalization fusion Generative Adversarial Network (EqGAN) for few-shot
image generation. Unlike existing fusion strategies that rely on either deep
features or local representations, we design two separate branches to fuse
structures and textures by disentangling encoded features into shallow and deep
contents. To refine image contents at all feature levels, we equalize the fused
structure and texture semantics at different scales and supplement the decoder
with richer information by skip connections. Since the fused structures and
textures may be inconsistent with each other, we devise a consistent
equalization loss between the equalized features and the intermediate output of
the decoder to further align the semantics. Comprehensive experiments on three
public datasets demonstrate that, EqGAN not only significantly improves
generation performance with FID score (by up to 32.7%) and LPIPS score (by up
to 4.19%), but also outperforms the state-of-the-arts in terms of accuracy (by
up to 1.97%) for downstream classification tasks.
