Few-shot Image Generation via Style Adaptation and Content Preservation
Xiaosheng He Fan Yang Fayao Liu Guosheng Lin
Training a generative model with limited data (e.g., 10) is a very
challenging task. Many works propose to fine-tune a pre-trained GAN model.
However, this can easily result in overfitting. In other words, they manage to
adapt the style but fail to preserve the content, where \textit{style} denotes
the specific properties that defines a domain while \textit{content} denotes
the domain-irrelevant information that represents diversity. Recent works try
to maintain a pre-defined correspondence to preserve the content, however, the
diversity is still not enough and it may affect style adaptation. In this work,
we propose a paired image reconstruction approach for content preservation. We
propose to introduce an image translation module to GAN transferring, where the
module teaches the generator to separate style and content, and the generator
provides training data to the translation module in return. Qualitative and
quantitative experiments show that our method consistently surpasses the
state-of-the-art methods in few shot setting.