Few-Shot Adaptation of Generative Adversarial Networks
This addresses the challenge of data-efficient image synthesis for domains with limited training data, though it appears incremental as it builds on existing GAN adaptation techniques.
The paper tackles the problem of adapting Generative Adversarial Networks (GANs) to new domains with very few images (5-100), proposing FSGAN, which achieves significant visual quality gains compared to existing methods.
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.