文献类型：期刊文献

英文题名：WaveGAN: Frequency-Aware GAN for High-Fidelity Few-Shot Image Generation

作者：Yang, Mengping[1,2]; Wang, Zhe[1,2]; Chi, Ziqiu[1,2]; Feng, Wenyi[1,2]

机构：[1] Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China; [2] Key Laboratory of Smart Manufacturing in Energy Chemical Process, East China University of Science and Technology, Shanghai, China

年份：2022

卷号：13675 LNCS

起止页码：1

外文期刊名：Lecture Notes in Computer Science

收录：EI(收录号：20224813184657)

基金：Acknowledgments. This work is supported by Shanghai Science and Technology Program “Distributed and generative few-shot algorithm and theory research” under Grant No. 20511100600 and “Federated based cross-domain and cross-task incremental learning” under Grant No. 21511100800, Natural Science Foundation of China under Grant No. 62076094, Chinese Defense Program of Science and Technology under Grant No. 2021-JCJQ-JJ-0041, China Aerospace Science and Technology Corporation Industry-University-Research Cooperation Foundation of the Eighth Research Institute under Grant No. SAST2021-007.

语种：英文

外文关键词：Computer vision - Generative adversarial networks

摘要：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. ? 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.