  Unpaired image-to-image translation methods aim at learning a mapping of
images from a source domain to a target domain. Recently, these methods proved
to be very useful in biological applications to display subtle phenotypic cell
variations otherwise invisible to the human eye. However, current models
require a large number of images to be trained, while mostmicroscopy
experiments remain limited in the number of images they can produce. In this
work, we present an improved CycleGAN architecture that employs self-supervised
discriminators to alleviate the need for numerous images. We demonstrate
quantitatively and qualitatively that the proposed approach outperforms the
CycleGAN baseline, including when it is combined with differentiable
augmentations. We also provide results obtained with small biological datasets
on obvious and non-obvious cell phenotype variations, demonstrating a
straightforward application of this method.
