Semantic-Aware Generator and Low-level Feature Augmentation for Few-shot Image Generation

Zhe Wang, Jiaoyan Guan, Mengping Yang, Ting Xiao, Ziqiu Chi

Abstract: Few-shot image generation aims to generate novel images for an unseen category with only a few samples. Prior studies fail to produce novel images with desirable diversity and fidelity. To ameliorate the generation quality, we in this paper propose a Semantic-Aware Generator (SAG) to provide explicit semantic guidance to the discriminator, and a Low-level Feature Augmentation (LFA) technique to provide fine-grained information, facilitating the diversity. Specifically, we observe that the generator feature layers contain different levels of semantic information. Such observation motivates us to employ intermediate feature maps of the generator as semantic labels to guide the discriminator, improving the semantic awareness of the generator. Moreover, spatially informative and diverse features obtained via LFA contribute to better generation quality. Together with the aforementioned module, we conduct extensive experiments on three representative benchmarks and the results demonstrate the effectiveness and advancement of our method.