Few-Shot Image Generation via Style Adaptation and Content Preservation.

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• Training generative models with very limited data (e.g., 10 samples) is a significant challenge.
• Fine-tuning pretrained Generative Adversarial Networks (GANs) often leads to overfitting, where style adaptation occurs at the expense of content preservation.
• Existing methods for content preservation in few-shot GANs struggle with sufficient diversity and may hinder style adaptation.

Purpose of the Study:

• To develop a novel approach for content preservation in generative models trained with limited data.
• To enhance the style adaptation capabilities of GANs while maintaining the integrity of the original content.
• To overcome the limitations of existing few-shot learning techniques for generative modeling.

Main Methods:

• Proposing a paired image reconstruction approach to explicitly preserve content during GAN training.
• Introducing an image translation module integrated into the GAN transferring process.
• Enabling the generator to learn style-content separation by facilitating a symbiotic relationship with the translation module.

Main Results:

• Demonstrated superior performance compared to state-of-the-art methods in few-shot learning settings.
• Achieved effective content preservation while successfully adapting to the target domain's style.
• Qualitative and quantitative experiments validated the method's effectiveness and robustness.

Conclusions:

• The proposed paired image reconstruction approach offers a robust solution for few-shot generative modeling.
• This method successfully balances style adaptation and content preservation, addressing key limitations in current GAN research.
• The findings suggest a promising direction for training generative models with minimal data.