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Few-shot domain adaptation to multiple domains aims to learn a complex imagedistribution across multiple domains from a few training images. A na\"ivesolution here is to train a separate model for each domain using few-shotdomain adaptation methods. Unfortunately, this approach mandateslinearly-scaled computational resources both in memory and computation timeand, more importantly, such separate models cannot exploit the shared knowledgebetween target domains. In this paper, we propose DynaGAN, a novel few-shotdomain-adaptation method for multiple target domains. DynaGAN has an adaptationmodule, which is a hyper-network that dynamically adapts a pretrained GAN modelinto the multiple target domains. Hence, we can fully exploit the sharedknowledge across target domains and avoid the linearly-scaled computationalrequirements. As it is still computationally challenging to adapt a large-sizeGAN model, we design our adaptation module light-weight using the rank-1 tensordecomposition. Lastly, we propose a contrastive-adaptation loss suitable formulti-domain few-shot adaptation. We validate the effectiveness of our methodthrough extensive qualitative and quantitative evaluations.

DynaGAN: Dynamic Few-shot Adaptation of GANs to Multiple Domains