  Recent advances in generative models and adversarial training have led to a
flourishing image-to-image (I2I) translation literature. The current I2I
translation approaches require training images from the two domains that are
either all paired (supervised) or all unpaired (unsupervised). In practice,
obtaining paired training data in sufficient quantities is often very costly
and cumbersome. Therefore solutions that employ unpaired data, while less
accurate, are largely preferred. In this paper, we aim to bridge the gap
between supervised and unsupervised I2I translation, with application to
semantic image segmentation. We build upon pix2pix and CycleGAN,
state-of-the-art seminal I2I translation techniques. We propose a method to
select (very few) paired training samples and achieve significant improvements
in both supervised and unsupervised I2I translation settings over random
selection. Further, we boost the performance by incorporating both (selected)
paired and unpaired samples in the training process. Our experiments show that
an extremely weak supervised I2I translation solution using only one paired
training sample can achieve a quantitative performance much better than the
unsupervised CycleGAN model, and comparable to that of the supervised pix2pix
model trained on thousands of pairs.
