  In the last few years, unpaired image-to-image translation has witnessed
remarkable progress. Although the latest methods are able to generate realistic
images, they crucially rely on a large number of labeled images. Recently, some
methods have tackled the challenging setting of few-shot image-to-image
translation, reducing the labeled data requirements for the target domain
during inference. In this work, we go one step further and reduce the amount of
required labeled data also from the source domain during training. To do so, we
propose applying semi-supervised learning via a noise-tolerant pseudo-labeling
procedure. We also apply a cycle consistency constraint to further exploit the
information from unlabeled images, either from the same dataset or external.
Additionally, we propose several structural modifications to facilitate the
image translation task under these circumstances. Our semi-supervised method
for few-shot image translation, called SEMIT, achieves excellent results on
four different datasets using as little as 10% of the source labels, and
matches the performance of the main fully-supervised competitor using only 20%
labeled data. Our code and models are made public at:
https://github.com/yaxingwang/SEMIT.
