论文标题
生成对抗网络的几乎没有改编
Few-Shot Adaptation of Generative Adversarial Networks
论文作者
论文摘要
生成的对抗网络（GAN）在图像合成任务中表现出色，但通常需要大量的训练样本才能实现高质量的合成。本文提出了一种简单有效的方法，即少量gan（fsgan），用于在几个射击设置（少于100张图像）中调整甘恩。 FSGAN重新利用组件分析技术，并学会适应预训练的权重的奇异值，同时冷冻相应的奇异载体。这为适应性提供了高度表达的参数空间，同时将变化变化对预审计的权重。我们在目标域中的5-100张图像的挑战性设置中验证了我们的方法。我们表明，与现有的GAN适应方法相比，我们的方法具有显着的视觉质量增长。我们报告定性和定量结果，显示了我们方法的有效性。我们还强调了在数据有效图像合成作品使用的标准定量度量中几乎没有合成的问题。代码和其他结果可在http://e-271.github.io/few-shot-gan上找到。
Generative Adversarial Networks (GANs) have shown remarkable performance in image synthesis tasks, but typically require a large number of training samples to achieve high-quality synthesis. This paper proposes a simple and effective method, Few-Shot GAN (FSGAN), for adapting GANs in few-shot settings (less than 100 images). FSGAN repurposes component analysis techniques and learns to adapt the singular values of the pre-trained weights while freezing the corresponding singular vectors. This provides a highly expressive parameter space for adaptation while constraining changes to the pretrained weights. We validate our method in a challenging few-shot setting of 5-100 images in the target domain. We show that our method has significant visual quality gains compared with existing GAN adaptation methods. We report qualitative and quantitative results showing the effectiveness of our method. We additionally highlight a problem for few-shot synthesis in the standard quantitative metric used by data-efficient image synthesis works. Code and additional results are available at http://e-271.github.io/few-shot-gan.
