  Recent studies have shown remarkable success in image-to-image translation
for attribute transfer applications. However, most of existing approaches are
based on deep learning and require an abundant amount of labeled data to
produce good results, therefore limiting their applicability. In the same vein,
recent advances in meta-learning have led to successful implementations with
limited available data, allowing so-called few-shot learning.
  In this paper, we address this limitation of supervised methods, by proposing
a novel approach based on GANs. These are trained in a meta-training manner,
which allows them to perform image-to-image translations using just a few
labeled samples from a new target class. This work empirically demonstrates the
potential of training a GAN for few shot image-to-image translation on hair
color attribute synthesis tasks, opening the door to further research on
generative transfer learning.
