Few-Shot Generative Models: A Style and Content Trade-Off

In this article, the authors explore the challenge of few-shot style transfer, which involves adapting a style to a new domain with only a handful of training examples. They propose a novel approach called Content Adaptation (CA), which balances content preservation and style adaptation by estimating the content information in a suitable way for each source → target adaptation.
The authors begin by acknowledging that different adaptations may require different trade-off points, and therefore, there is no one-size-fits-all solution for few-shot style transfer. They then introduce CA, which combines the strengths of two existing methods: Conditional Diffusion Convolution (CDC) and RSSA (Region-based Style Transfer via Adaptation). CDC focuses on content preservation, while RSSA prioritizes style adaptation.
To implement CA, the authors use a discriminator to preserve content and a generator to adapt the style. They then propose a new way of estimating the content information, which involves maintaining a correspondence between two images. This allows the author to estimate the true content and balance it with the adapted style.
The authors evaluate their method on several benchmark datasets and show that it consistently outperforms existing methods in few-shot settings. They also demonstrate that their approach produces more diverse and higher-quality results than CDC and RSSA, while avoiding overfitting.
To measure the diversity level, the authors use an intra-cluster pairwise LPIPS distance metric [12]. They show that their method achieves higher average LPIPS distances than CDC, indicating more diverse results. On the other hand, although RSSA achieves higher LPIPS distances in some adaptations, it often produces poor FID scores (meaning the learned distribution is very different from the target domain).
The authors conclude that their approach provides a promising solution for few-shot style transfer, and they believe it has important implications for real-world applications where content adaptation is crucial. They also highlight the potential of their method to be combined with other techniques, such as semi-supervised learning or weakly supervised learning, to further improve performance.
In summary, this article presents a novel approach called Content Adaptation (CA) that balances content preservation and style adaptation for few-shot style transfer. CA combines the strengths of CDC and RSSA, and it uses a new way of estimating the content information to achieve more diverse and higher-quality results than existing methods. The authors evaluate their method on several benchmark datasets and show that it consistently outperforms existing methods in few-shot settings.