# One Size does not Fit All: Heterogeneous Latent Space Alignment for Unsupervised Domain Adaptation Source: [One Size does not Fit All: Heterogeneous Latent Space Alignment for Unsupervised Domain Adaptation]() Collection index: [llms.txt]() Data updated: 2026-06-24T05:00:13.022Z Authors: Evangelia Koskinioti, Yi Shen, Georgios Stamou, Michael M. Zavlanos Published: 2026-06-19T13:33:38.000Z ## Abstract Domain shift remains a major obstacle to the reliable deployment of machine learning models in high-stakes environments such as healthcare. While Domain adaptation aims to mitigate these effects, existing approaches suffer from limited expressiveness of latent representations and a reliance on handcrafted, static augmentations. In this work, we address these limitations by proposing a novel deep learning architecture for Unsupervised Domain Adaptation (UDA), specifically optimized for medical image segmentation. Our framework, ADualVUOT, integrates a dual-encoder Variational Autoencoder (VAE) with Continuous Normalizing Flows (CNFs) to increase modeling flexibility and posterior expressiveness. To achieve domain alignment, we leverage Unbalanced Optimal Transport (UOT) through the Gaussian-Gromov-Wasserstein (GGW) distance, which handles structural and topological discrepancies between domains. Furthermore, we incorporate an adversarial augmentation scheme to synthesize worst-case compositions, thus enhancing model robustness. Extensive experiments on medical imaging benchmarks show significant gains over prior OT-based approaches. ## 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. - [Cardiac Structure Segmentation on MM-WHS MRI to CT adaptation (test)](): Cardiac structure segmentation; MM-WHS MRI to CT adaptation (test); AA Score: 93.1. - [Prostate MRI Segmentation on Multi-Site Prostate MRI](): Prostate MRI Segmentation; Multi-Site Prostate MRI; Metric A: 92.3. - [Semantic Segmentation on MM-WHS CT to MRI adaptation](): Semantic segmentation; MM-WHS CT to MRI adaptation; AA Score: 76.7. ## Original sources and implementation [Original paper]() [PDF]()