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.

我们提出了一种配对图像重建方法，用于内容保留，通过引入图像转换模块到生成对抗网络转换过程中，在少样本设置中，定性和定量实验证明我们的方法始终优于现有方法。

通过风格调整和内容保留实现少样本图像生成