Training GANs in low-data regimes remains a challenge, as overfitting often
leads to memorization or training divergence. In this work, we introduce
One-Shot GAN that can learn to generate samples from a training set as little
as one image or one video. We propose a two-branch discriminator, with content
and layout branches designed to judge the internal content separately from the
scene layout realism. This allows synthesis of visually plausible, novel
compositions of a scene, with varying content and layout, while preserving the
context of the original sample. Compared to previous single-image GAN models,
One-Shot GAN achieves higher diversity and quality of synthesis. It is also not
restricted to the single image setting, successfully learning in the introduced
setting of a single video.

本文提出了一种称为 One-Shot GAN 的算法，可从仅有一张图像或短视频的训练集中进行学习，利用两个判别器实现对内部内容和场景布局真实性的分开评估，并在保留原始样本内容的基础上构造出视觉上逼真、新颖的场景组合合成，相较于之前的单图像 GAN 模型具有更高的多样性和合成质量，同时也能够在单个视频场景中有效学习。