DynaGAN: Dynamic Few-shot Adaptation of GANs to Multiple Domains
This addresses the challenge of adapting GANs to new domains with limited data, which is incremental as it builds on existing few-shot adaptation methods.
The paper tackles the problem of few-shot domain adaptation for GANs across multiple domains by proposing DynaGAN, which uses a hyper-network to dynamically adapt a pretrained GAN, achieving competitive results with reduced computational costs.
Few-shot domain adaptation to multiple domains aims to learn a complex image distribution across multiple domains from a few training images. A naïve solution here is to train a separate model for each domain using few-shot domain adaptation methods. Unfortunately, this approach mandates linearly-scaled computational resources both in memory and computation time and, more importantly, such separate models cannot exploit the shared knowledge between target domains. In this paper, we propose DynaGAN, a novel few-shot domain-adaptation method for multiple target domains. DynaGAN has an adaptation module, which is a hyper-network that dynamically adapts a pretrained GAN model into the multiple target domains. Hence, we can fully exploit the shared knowledge across target domains and avoid the linearly-scaled computational requirements. As it is still computationally challenging to adapt a large-size GAN model, we design our adaptation module light-weight using the rank-1 tensor decomposition. Lastly, we propose a contrastive-adaptation loss suitable for multi-domain few-shot adaptation. We validate the effectiveness of our method through extensive qualitative and quantitative evaluations.