# Unpaired Image-to-Image Translation with Limited Data to Reveal Subtle Phenotypes

arXiv: https://arxiv.org/abs/2302.08503 · HTML: https://mlsanity.com/paper/2302.08503/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 31 references that are in the map.
- First posted on arXiv 21 Jan 2023.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [Training Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2006.06676/) (2020)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Differentiable Augmentation for Data-Efficient GAN Training](https://mlsanity.com/paper/2006.10738/) (2020)
- [DualGAN: Unsupervised Dual Learning for Image-to-Image Translation](https://mlsanity.com/paper/1704.02510/) (2017)
- [Learning to Discover Cross-Domain Relations with Generative Adversarial Networks](https://mlsanity.com/paper/1703.05192/) (2017)
- [Revisiting Self-Supervised Visual Representation Learning](https://mlsanity.com/paper/1901.09005/) (2019)
- [Few-Shot Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1905.01723/) (2019)
- [Few-shot Image Generation via Cross-domain Correspondence](https://mlsanity.com/paper/2104.06820/) (2021)
- [Self-Supervised Representation Learning: Introduction, Advances and Challenges](https://mlsanity.com/paper/2110.09327/) (2021)
- [Boosting Few-Shot Visual Learning with Self-Supervision](https://mlsanity.com/paper/1906.05186/) (2019)
- [Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis](https://mlsanity.com/paper/2101.04775/) (2021)
- [Transferring GANs: generating images from limited data](https://mlsanity.com/paper/1805.01677/) (2018)
- [Unsupervised Cross-Domain Image Generation](https://mlsanity.com/paper/1611.02200/) (2016)
- [Self-Supervised GANs via Auxiliary Rotation Loss](https://mlsanity.com/paper/1811.11212/) (2018)
- [Image Augmentations for GAN Training](https://mlsanity.com/paper/2006.02595/) (2020)
- [Stylized Neural Painting](https://mlsanity.com/paper/2011.08114/) (2020)
- [TraVeLGAN: Image-to-image Translation by Transformation Vector Learning](https://mlsanity.com/paper/1902.09631/) (2019)
- [Improving Shape Deformation in Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1808.04325/) (2018)
- [Deceiving Image-to-Image Translation Networks for Autonomous Driving with Adversarial Perturbations](https://mlsanity.com/paper/2001.01506/) (2020)

## Built on by (most cited first, 7 of 7)

- [ChAda-ViT : Channel Adaptive Attention for Joint Representation Learning of Heterogeneous Microscopy Images](https://mlsanity.com/paper/2311.15264/) (2023)
- [PhenDiff: Revealing Subtle Phenotypes with Diffusion Models in Real Images](https://mlsanity.com/paper/2312.08290/) (2023)
- [No Free Lunch in Self Supervised Representation Learning](https://mlsanity.com/paper/2304.11718/) (2023)
- [GANs Conditioning Methods: A Survey](https://mlsanity.com/paper/2408.15640/) (2024)
- [The Quest for Early Detection of Retinal Disease: 3D CycleGAN-based Translation of Optical Coherence Tomography into Confocal Microscopy](https://mlsanity.com/paper/2408.04091/) (2024)
- [DiffEx: Explaining a Classifier with Diffusion Models to Identify Microscopic Cellular Variations](https://mlsanity.com/paper/2502.09663/) (2025)
- [Revealing Subtle Phenotypes in Small Microscopy Datasets Using Latent Diffusion Models](https://mlsanity.com/paper/2502.09665/) (2025)

## Abstract (arXiv)

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.