论文标题
Wavegan：高保真性的频率感知gan几乎没有图像产生
WaveGAN: Frequency-aware GAN for High-Fidelity Few-shot Image Generation
论文作者
论文摘要
现有的少量图像生成方法通常在图像或特征级别采用基于融合的策略来产生新图像。但是，先前的方法很难通过细节良好的细节综合高频信号，从而恶化了合成质量。为了解决这个问题，我们提出了Wovegan，这是一种用于几弹图像生成的频率感知模型。具体而言，我们将编码的特征分解为多个频率组件，并执行低频跳过连接以保留轮廓和结构信息。然后，我们通过使用高频跳过连接来减轻发电机综合细节的斗争，从而为发电机提供信息频率信息。此外，我们在生成的图像和真实图像上利用频率L1损失来进一步阻碍频率信息丢失。广泛的实验证明了我们方法在三个数据集上的有效性和进步。值得注意的是，我们以FID 42.17，LPIPS 0.3868，FID 30.35，LPIPS 0.5076和FID 4.96，LPIPS，LPIPS分别为0.3822，在花朵，动物脸和VGGFECE上分别实现了新的最先进。 github：https：//github.com/kobeshegu/eccv2022_wavegan
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
