# Unsupervised Domain Transfer with Conditional Invertible Neural Networks

arXiv: https://arxiv.org/abs/2303.10191 · HTML: https://mlsanity.com/paper/2303.10191/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 17 Mar 2023.

## Builds on (most cited first, 12 of 12)

- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [CyCADA: Cycle-Consistent Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.03213/) (2017)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017)
- [Adversarial Feature Hallucination Networks for Few-Shot Learning](https://mlsanity.com/paper/2003.13193/) (2020)
- [AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows](https://mlsanity.com/paper/1905.12892/) (2019)
- [Deep learning for biomedical photoacoustic imaging: A review](https://mlsanity.com/paper/2011.02744/) (2020)
- [Uncertainty-aware performance assessment of optical imaging modalities with invertible neural networks](https://mlsanity.com/paper/1903.03441/) (2019)
- [Semantic segmentation of multispectral photoacoustic images using deep learning](https://mlsanity.com/paper/2105.09624/) (2021)
- [CDCGen: Cross-Domain Conditional Generation via Normalizing Flows and Adversarial Training](https://mlsanity.com/paper/2108.11368/) (2021)
- [Photoacoustic image synthesis with generative adversarial networks](https://mlsanity.com/paper/2103.15510/) (2021)
- [Band selection for oxygenation estimation with multispectral/hyperspectral imaging](https://mlsanity.com/paper/1905.11297/) (2019)

## Built on by (most cited first, 4 of 4)

- [Moving beyond simulation: data-driven quantitative photoacoustic imaging using tissue-mimicking phantoms](https://mlsanity.com/paper/2306.06748/) (2023)
- [Optimizing Retinal Prosthetic Stimuli with Conditional Invertible Neural Networks](https://mlsanity.com/paper/2403.04884/) (2024)
- [Distribution-informed and wavelength-flexible data-driven photoacoustic oximetry](https://mlsanity.com/paper/2403.14863/) (2024)
- [Clinical Validation of Deep Learning for Real-Time Tissue Oxygenation Estimation Using Spectral Imaging](https://mlsanity.com/paper/2505.18010/) (2025)

## Abstract (arXiv)

Synthetic medical image generation has evolved as a key technique for neural network training and validation. A core challenge, however, remains in the domain gap between simulations and real data. While deep learning-based domain transfer using Cycle Generative Adversarial Networks and similar architectures has led to substantial progress in the field, there are use cases in which state-of-the-art approaches still fail to generate training images that produce convincing results on relevant downstream tasks. Here, we address this issue with a domain transfer approach based on conditional invertible neural networks (cINNs). As a particular advantage, our method inherently guarantees cycle consistency through its invertible architecture, and network training can efficiently be conducted with maximum likelihood training. To showcase our method's generic applicability, we apply it to two spectral imaging modalities at different scales, namely hyperspectral imaging (pixel-level) and photoacoustic tomography (image-level). According to comprehensive experiments, our method enables the generation of realistic spectral data and outperforms the state of the art on two downstream classification tasks (binary and multi-class). cINN-based domain transfer could thus evolve as an important method for realistic synthetic data generation in the field of spectral imaging and beyond.