HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks (2210.08884v4)

Abstract: Domain adaptation framework of GANs has achieved great progress in recent years as a main successful approach of training contemporary GANs in the case of very limited training data. In this work, we significantly improve this framework by proposing an extremely compact parameter space for fine-tuning the generator. We introduce a novel domain-modulation technique that allows to optimize only 6 thousand-dimensional vector instead of 30 million weights of StyleGAN2 to adapt to a target domain. We apply this parameterization to the state-of-art domain adaptation methods and show that it has almost the same expressiveness as the full parameter space. Additionally, we propose a new regularization loss that considerably enhances the diversity of the fine-tuned generator. Inspired by the reduction in the size of the optimizing parameter space we consider the problem of multi-domain adaptation of GANs, i.e. setting when the same model can adapt to several domains depending on the input query. We propose the HyperDomainNet that is a hypernetwork that predicts our parameterization given the target domain. We empirically confirm that it can successfully learn a number of domains at once and may even generalize to unseen domains. Source code can be found at https://github.com/MACderRu/HyperDomainNet

• The paper introduces a domain-modulation technique that reduces the adaptation parameter space from 30M to only 6,000 dimensions.
• The method incorporates a new regularization loss to enhance generator diversity and prevent mode collapse during fine-tuning.
• HyperDomainNet, a hypernetwork, generalizes across unseen domains, offering significant computational savings and robust adaptability.

Overview of "HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks"

The paper "HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks" introduces a method aimed at enhancing the domain adaptation capabilities of Generative Adversarial Networks (GANs), particularly focusing on StyleGAN2. Traditional GANs, when fine-tuned for domain adaptation, typically require substantial amounts of training data and computational resources. The paper addresses this by proposing a novel and efficient parameter space for fine-tuning generators, allowing domain adaptation with significantly fewer trainable parameters.

Methodological Contributions

The authors propose a domain-modulation technique, which introduces a new parameterization strategy for the StyleGAN2 generator, reducing the adaptation parameter space from approximately 30 million parameters to just 6,000 dimensions. This approach leverages a modulation operation across convolutional layers within the generator, effectively substituting the necessity to modify all generator weights during domain adaptation. The domain-modulation operation is integrated with the existing modulation/demodulation mechanism of StyleGAN2, aligning with strategic channel-wise style transformations.

Furthermore, the paper offers a new regularization loss, aimed at increasing the diversity of fine-tuned generators, tackling the persistent issue of mode collapse during domain adaptation. This regularization is critical in maintaining the expressiveness of the generator when undergoing substantial parameter reduction.

Experimental Framework and Results

The paper assesses the efficacy of the proposed domain-modulation technique by applying it to existing state-of-the-art domain adaptation methods such as StyleGAN-NADA and MindTheGAP. The key findings reveal that the reduced parameter space maintains comparable effectiveness in terms of adaptation quality and diversity.

An extension of this technique is presented via HyperDomainNet, a hypernetwork designed to predict parameterizations suitable for multiple domain adaptations based on input domain queries. HyperDomainNet exhibits the ability to generalize beyond trained target domains, demonstrating proficiency in adapting to unseen domains with training that involves numerous domain descriptions.

Extensive empirical evaluations were conducted across diverse domains, demonstrating competitive performance of HyperDomainNet against separate domain-specific models. The experimental results underscore the potential for considerable computational savings without sacrificing model expressiveness, as well as robust generalization across domains.

Implications and Future Directions

The contributions of this paper have several practical implications. Primarily, the drastic reduction in parameters needed for GAN adaptation suggests significant savings in both computational cost and training time, broadening the feasibility of deploying GANs in resource-limited environments or scenarios where rapid domain adaptation is required. Furthermore, the ability to adapt a single model to multiple domains concurrently opens new avenues for applications in creative industries and real-time generative content generation, benefiting systems that require dynamic adaptability to various styles or content types.

Theoretically, this work strengthens the understanding of parameter space relevance in GAN's adaptability, providing a stepping-stone for further research into efficient domain adaptation paradigms. Future directions could explore applying the domain-modulation technique to other generative model architectures or investigating the integration of this approach with other few-shot learning frameworks. Additionally, enhancing the robustness of HyperDomainNet in adapting to radically different domains would further augment its versatility and application scope.