# Unpaired Image-to-Image Translation with Limited Data to Reveal Subtle Phenotypes Source: [Unpaired Image-to-Image Translation with Limited Data to Reveal Subtle Phenotypes]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:59:52.182Z Authors: Anis Bourou, Auguste Genovesio Published: 2023-01-21T16:25:04.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image-to-Image Translation on BBBC021 Latrunculin B treatment v1](): Image-to-Image Translation; BBBC021 Latrunculin B treatment v1; FID (C1): 31.59. ## Original sources and implementation [Original paper]() [PDF]()