Smoothness Similarity Regularization for Few-Shot GAN Adaptation

• URL: http://arxiv.org/abs/2308.09717v1
• Date: Fri, 18 Aug 2023 17:59:53 GMT
• Title: Smoothness Similarity Regularization for Few-Shot GAN Adaptation
• Authors: Vadim Sushko, Ruyu Wang, Juergen Gall
• Abstract summary: We propose a new smoothness similarity regularization that transfers the inherently learned smoothness of the pre-trained GAN to the few-shot target domain. We evaluate our approach by adapting an unconditional and a class-conditional GAN to diverse few-shot target domains.
• Score: 16.92497517282215
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The task of few-shot GAN adaptation aims to adapt a pre-trained GAN model to a small dataset with very few training images. While existing methods perform well when the dataset for pre-training is structurally similar to the target dataset, the approaches suffer from training instabilities or memorization issues when the objects in the two domains have a very different structure. To mitigate this limitation, we propose a new smoothness similarity regularization that transfers the inherently learned smoothness of the pre-trained GAN to the few-shot target domain even if the two domains are very different. We evaluate our approach by adapting an unconditional and a class-conditional GAN to diverse few-shot target domains. Our proposed method significantly outperforms prior few-shot GAN adaptation methods in the challenging case of structurally dissimilar source-target domains, while performing on par with the state of the art for similar source-target domains.

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