  Given a large number of training samples, GANs can achieve remarkable
performance for the image synthesis task. However, training GANs in extremely
low-data regimes remains a challenge, as overfitting often occurs, leading to
memorization or training divergence. In this work, we introduce One-Shot GAN,
an unconditional generative model that can learn to generate samples from a
single training image or a single video clip. We propose a two-branch
discriminator architecture, with content and layout branches designed to judge
internal content and scene layout realism separately from each other. 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 generates
more diverse, higher quality images, while also not being restricted to a
single image setting. We show that our model successfully deals with other
one-shot regimes, and introduce a new task of learning generative models from a
single video.
