  Existing few-shot image generation approaches typically employ fusion-based
strategies, either on the image or the feature level, to produce new images.
However, previous approaches struggle to synthesize high-frequency signals with
fine details, deteriorating the synthesis quality. To address this, we propose
WaveGAN, a frequency-aware model for few-shot image generation. Concretely, we
disentangle encoded features into multiple frequency components and perform
low-frequency skip connections to preserve outline and structural information.
Then we alleviate the generator's struggles of synthesizing fine details by
employing high-frequency skip connections, thus providing informative frequency
information to the generator. Moreover, we utilize a frequency L1-loss on the
generated and real images to further impede frequency information loss.
Extensive experiments demonstrate the effectiveness and advancement of our
method on three datasets. Noticeably, we achieve new state-of-the-art with FID
42.17, LPIPS 0.3868, FID 30.35, LPIPS 0.5076, and FID 4.96, LPIPS 0.3822
respectively on Flower, Animal Faces, and VGGFace. GitHub:
https://github.com/kobeshegu/ECCV2022_WaveGAN
