Few-shot Generative Domain Adaptation (GDA)
Addressing the challenge of adapting a model trained on a source domain to perform well on a target domain, using only a few examples from the target domain.
Main Solution: Improving the Generator
Focuses on enhancing a special AI model called a “generator” to create new data samples resembling the target domain with only a few examples.
Challenges and DoRM Solution
Challenges arise when source and target domains have significant differences. Domain Re-Modulation (DoRM) addresses these challenges by enhancing image synthesis quality, diversity, and cross-domain consistency.
Key Innovations of DoRM
- Source Generator Preparation
- Introducing M&A Modules
- Style Space Adjustment
- Linear Domain Shift
- Cross-Domain Consistency Enhancement
- Training Framework
Evaluation and Conclusion
DoRM demonstrated superior synthesis quality and cross-domain consistency compared to other methods, especially in domains like Sketches and FFHQ-Babies. It excelled in multi-domain and hybrid-domain generation, showcasing its ability to integrate diverse domains and synthesize novel hybrid outputs efficiently.
DoRM: A Brain-Inspired Approach to Generative Domain Adaptation
DoRM is a streamlined generator structure tailored for GDA, incorporating a novel similarity-based structure loss to ensure robust cross-domain consistency. The method demonstrates superior synthesis quality, diversity, and cross-domain consistency compared to existing approaches.
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