Smoothness Similarity Regularization for Few-Shot GAN Adaptation

Vadim Sushko; Ruyu Wang; Juergen Gall

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arXiv · 2308.09717

Smoothness Similarity Regularization for Few-Shot GAN Adaptation

Vadim Sushko·Ruyu Wang·Juergen Gall

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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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BibTeX RIS

Vadim Sushko, Ruyu Wang, Juergen Gall. 2023-08-18. Smoothness Similarity Regularization for Few-Shot GAN Adaptation. https://arxiv.org/abs/2308.09717

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