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

• URL: http://arxiv.org/abs/2211.14554v1
• Date: Sat, 26 Nov 2022 12:46:40 GMT
• Title: DynaGAN: Dynamic Few-shot Adaptation of GANs to Multiple Domains
• Authors: Seongtae Kim, Kyoungkook Kang, Geonung Kim, Seung-Hwan Baek, Sunghyun Cho
• Abstract summary: Few-shot domain adaptation to multiple domains aims to learn a complex image distribution across multiple domains from a few training images. We propose DynaGAN, a novel few-shot domain-adaptation method for multiple target domains.
• Score: 26.95350186287616
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Few-shot domain adaptation to multiple domains aims to learn a complex image distribution across multiple domains from a few training images. A na\"ive 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.

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