Few-shot Image Generation via Style Adaptation and Content Preservation
This work addresses the challenge of generating diverse images with limited data for applications in computer vision and generative modeling, representing an incremental improvement over existing methods.
The paper tackles the problem of few-shot image generation by addressing overfitting in fine-tuned GANs, proposing a paired image reconstruction approach that separates style and content to improve diversity and style adaptation, resulting in consistent outperformance over state-of-the-art methods in qualitative and quantitative experiments.
Training a generative model with limited data (e.g., 10) is a very challenging task. Many works propose to fine-tune a pre-trained GAN model. However, this can easily result in overfitting. In other words, they manage to adapt the style but fail to preserve the content, where \textit{style} denotes the specific properties that defines a domain while \textit{content} denotes the domain-irrelevant information that represents diversity. Recent works try to maintain a pre-defined correspondence to preserve the content, however, the diversity is still not enough and it may affect style adaptation. In this work, we propose a paired image reconstruction approach for content preservation. We propose to introduce an image translation module to GAN transferring, where the module teaches the generator to separate style and content, and the generator provides training data to the translation module in return. Qualitative and quantitative experiments show that our method consistently surpasses the state-of-the-art methods in few shot setting.