Few-shot generative domain adaptation (GDA) is a machine learning and domain adaptation concept that addresses the challenge of adapting a model trained in a source domain to work well in a target domain using only a few examples from the target domain. Such techniques are particularly useful when obtaining large amounts of labeled data from the target domain is costly or impractical.
The main existing solutions for GDA focus on improving special AI models called “generators”. Generators create new data samples that are similar to the target domain, even if there are only a few examples. Techniques such as consistency loss and GAN inversion help generators generate high-quality and diverse data. These methods ensure that the generated data accurately preserves similarities and differences across domains. However, challenges arise when there are significant differences between the source and target domains. In such cases, it remains a major challenge to enable generators to adapt and accurately generate data that fits both domains.
To address these challenges, a recent paper published at NeurIPS introduces Domain Remodulation (DoRM) for GDA. Unlike previous methods, DoRM integrates memory and domain association features inspired by human learning while enhancing the quality, diversity, and cross-domain consistency of image synthesis. By modifying the style space using novel mapping and affine modules, DoRM is able to generate high-fidelity images across multiple domains, including hybrids not seen in training. The authors also introduce a novel similarity-based structural loss that achieves better cross-domain alignment, and shows superior performance in experimental evaluation compared to existing approaches.
Specifically, DoRM will enhance the capabilities of GDA’s generators by introducing several key innovations.
1. Preparing the source generator: First, our method starts with a pre-trained StyleGAN2 generator, which serves as the basis for subsequent adaptations.
2. Introducing M&A Modules: As the source generator is fixed to adapt to new target domains, new Mapping and Affine (M&A) modules are introduced. These modules are crucial as they are specialized in capturing certain attributes unique to the target domain. Selective activation of these modules allows the generator to fine-tune its output to suit the nuances of different domains.
3. Style Space Adjustment: Transform the latent code from the source domain into a new space that matches the visual style of the target domain. This adjustment allows the generator to synthesize outputs that accurately reflect the characteristics of the target domain.
4. Linear Domain Shift: DoRM facilitates linearly combinable domain shifts in the generator's style space using multiple M&A modules. These modules allow precise tuning for specific domains, improving the generator's flexibility to synthesize images across different domains and seamlessly combine attributes from multiple training sources.
5. Cross-domain consistency enforcement: DoRM introduces a novel similarity-based structural loss (Lss) to ensure cross-domain consistency. This loss leverages CLIP image encoder tokens to refine the autocorrelation maps between source and target images, maintaining structural consistency and fidelity to the target domain characteristics in the generated output.
6. Training Framework: DoRM integrates a comprehensive loss function that combines StyleGAN2's original adversarial loss and Lss during training. This integrated framework optimizes the learning of the generator and discriminator, ensuring stable training dynamics and robust adaptation to complex domain shifts.
The research team evaluated the proposed DoRM method using the Flickr-Faces-HQ Dataset (FFHQ). A pre-trained StyleGAN2 model was applied to enable stable training with 10-shot GDA. DoRM showed superior synthesis quality and cross-domain consistency compared to other methods, especially in domains such as sketches and FFHQ-Babies. Quantitative metrics such as Fréchet Inception Distance (FID) and identity similarity consistently showed DoRM outperforming competitors. The method also excelled in multi-domain and hybrid domain generation, demonstrating the ability to integrate diverse domains and efficiently synthesize new hybrid outputs. Ablation studies confirmed the effectiveness of DoRM's generator structure across a range of experimental settings.
Finally, the research team introduces DoRM, a streamlined generator structure tailored for GDA. DoRM incorporates a novel similarity-based structural loss to ensure robust cross-domain consistency. Through rigorous evaluation, the method demonstrates superior synthesis quality, diversity, and cross-domain consistency compared to existing approaches. Similar to the human brain, DoRM integrates knowledge across domains, enabling the generation of images in new hybrid domains not encountered during training.
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Mahmood is a postdoctoral researcher in machine learning.
in Physical Sciences and an M.S.
Communications and network systems. His current field of expertise is
His research interests include computer vision, stock market prediction, and deep learning.
He has published several scientific papers on human relearning.
Identifying and researching ocean robustness and stability in deep seas
network.
