  Unsupervised image-to-image translation methods learn to map images in a
given class to an analogous image in a different class, drawing on unstructured
(non-registered) datasets of images. While remarkably successful, current
methods require access to many images in both source and destination classes at
training time. We argue this greatly limits their use. Drawing inspiration from
the human capability of picking up the essence of a novel object from a small
number of examples and generalizing from there, we seek a few-shot,
unsupervised image-to-image translation algorithm that works on previously
unseen target classes that are specified, at test time, only by a few example
images. Our model achieves this few-shot generation capability by coupling an
adversarial training scheme with a novel network design. Through extensive
experimental validation and comparisons to several baseline methods on
benchmark datasets, we verify the effectiveness of the proposed framework. Our
implementation and datasets are available at https://github.com/NVlabs/FUNIT .
