LADB: Latent Aligned Diffusion Bridges for Semi-Supervised Domain Translation

• URL: http://arxiv.org/abs/2509.08628v1
• Date: Wed, 10 Sep 2025 14:23:07 GMT
• Title: LADB: Latent Aligned Diffusion Bridges for Semi-Supervised Domain Translation
• Authors: Xuqin Wang, Tao Wu, Yanfeng Zhang, Lu Liu, Dong Wang, Mingwei Sun, Yongliang Wang, Niclas Zeller, Daniel Cremers,
• Abstract summary: Diffusion models excel at generating high-quality outputs but face challenges in data-scarce domains.<n>We propose Latent Aligned Diffusion Bridges (LADB), a semi-supervised framework for sample-to-sample translation.
• Score: 54.690154688667086
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Diffusion models excel at generating high-quality outputs but face challenges in data-scarce domains, where exhaustive retraining or costly paired data are often required. To address these limitations, we propose Latent Aligned Diffusion Bridges (LADB), a semi-supervised framework for sample-to-sample translation that effectively bridges domain gaps using partially paired data. By aligning source and target distributions within a shared latent space, LADB seamlessly integrates pretrained source-domain diffusion models with a target-domain Latent Aligned Diffusion Model (LADM), trained on partially paired latent representations. This approach enables deterministic domain mapping without the need for full supervision. Compared to unpaired methods, which often lack controllability, and fully paired approaches that require large, domain-specific datasets, LADB strikes a balance between fidelity and diversity by leveraging a mixture of paired and unpaired latent-target couplings. Our experimental results demonstrate superior performance in depth-to-image translation under partial supervision. Furthermore, we extend LADB to handle multi-source translation (from depth maps and segmentation masks) and multi-target translation in a class-conditioned style transfer task, showcasing its versatility in handling diverse and heterogeneous use cases. Ultimately, we present LADB as a scalable and versatile solution for real-world domain translation, particularly in scenarios where data annotation is costly or incomplete.

Related papers

• Towards General Modality Translation with Contrastive and Predictive Latent Diffusion Bridge [16.958159611661813]
  Latent Denoising Diffusion Bridge Model (LDDBM) is a general-purpose framework for modality translation.<n>By operating in a shared latent space, our method learns a bridge between arbitrary modalities without requiring aligned dimensions.<n>Our approach supports arbitrary modality pairs and performs strongly on diverse MT tasks, including multi-view to 3D shape generation, image super-resolution, and multi-view scene synthesis.
  arXiv  Detail & Related papers  (2025-10-23T17:59:54Z)
• Let Synthetic Data Shine: Domain Reassembly and Soft-Fusion for Single Domain Generalization [68.41367635546183]
  Single Domain Generalization aims to train models with consistent performance across diverse scenarios using data from a single source.<n>We propose Discriminative Domain Reassembly and Soft-Fusion (DRSF), a training framework leveraging synthetic data to improve model generalization.
  arXiv  Detail & Related papers  (2025-03-17T18:08:03Z)
• Multisource Collaborative Domain Generalization for Cross-Scene Remote Sensing Image Classification [57.945437355714155]
  Cross-scene image classification aims to transfer prior knowledge of ground materials to annotate regions with different distributions.<n>Existing approaches focus on single-source domain generalization to unseen target domains.<n>We propose a novel multi-source collaborative domain generalization framework (MS-CDG) based on homogeneity and heterogeneity characteristics of multi-source remote sensing data.
  arXiv  Detail & Related papers  (2024-12-05T06:15:08Z)
• Online Continual Domain Adaptation for Semantic Image Segmentation Using Internal Representations [28.549418215123936]
  We develop an online UDA algorithm for semantic segmentation of images that improves model generalization on unannotated domains. We evaluate our approach on well established semantic segmentation datasets and demonstrate it compares favorably against state-of-the-art (SOTA) semantic segmentation methods.
  arXiv  Detail & Related papers  (2024-01-02T04:48:49Z)
• Multiple Noises in Diffusion Model for Semi-Supervised Multi-Domain Translation [1.9510388605988505]
  We introduce Multi-Domain Diffusion (MDD) to solve the challenge of multi-domain translation.<n>MDD reconstructs missing views for new data objects, and enables learning in semi-supervised contexts.<n>We evaluate our approach through domain translation experiments on BL3NDT, a multi-domain synthetic dataset.
  arXiv  Detail & Related papers  (2023-09-25T15:31:16Z)
• Unsupervised Domain Adaptation via Domain-Adaptive Diffusion [31.802163238282343]
  Unsupervised Domain Adaptation (UDA) is quite challenging due to the large distribution discrepancy between the source domain and the target domain. Inspired by diffusion models which have strong capability to gradually convert data distributions across a large gap, we consider to explore the diffusion technique to handle the challenging UDA task. Our method outperforms the current state-of-the-arts by a large margin on three widely used UDA datasets.
  arXiv  Detail & Related papers  (2023-08-26T14:28:18Z)
• MADAv2: Advanced Multi-Anchor Based Active Domain Adaptation Segmentation [98.09845149258972]
  We introduce active sample selection to assist domain adaptation regarding the semantic segmentation task. With only a little workload to manually annotate these samples, the distortion of the target-domain distribution can be effectively alleviated. A powerful semi-supervised domain adaptation strategy is proposed to alleviate the long-tail distribution problem.
  arXiv  Detail & Related papers  (2023-01-18T07:55:22Z)
• Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning [122.62311703151215]
  Divide and Contrast (DaC) aims to connect the good ends of both worlds while bypassing their limitations. DaC divides the target data into source-like and target-specific samples, where either group of samples is treated with tailored goals. We further align the source-like domain with the target-specific samples using a memory bank-based Maximum Mean Discrepancy (MMD) loss to reduce the distribution mismatch.
  arXiv  Detail & Related papers  (2022-11-12T09:21:49Z)
• Collaborative Semantic Aggregation and Calibration for Federated Domain Generalization [28.573872986524794]
  DG aims to learn from multiple known source domains a model that can generalize well to unknown target domains. In this paper, we tackle the problem of federated domain generalization where the source datasets can only be accessed locally. We conduct data-free semantic aggregation by fusing the models trained on separated domains layer-by-layer.
  arXiv  Detail & Related papers  (2021-10-13T14:08:29Z)
• Multi-Source domain adaptation via supervised contrastive learning and confident consistency regularization [0.0]
  Multi-Source Unsupervised Domain Adaptation (multi-source UDA) aims to learn a model from several labeled source domains. We propose Contrastive Multi-Source Domain Adaptation (CMSDA) for multi-source UDA that addresses this limitation.
  arXiv  Detail & Related papers  (2021-06-30T14:39:15Z)
• Dual Distribution Alignment Network for Generalizable Person Re-Identification [174.36157174951603]
  Domain generalization (DG) serves as a promising solution to handle person Re-Identification (Re-ID) We present a Dual Distribution Alignment Network (DDAN) which handles this challenge by selectively aligning distributions of multiple source domains. We evaluate our DDAN on a large-scale Domain Generalization Re-ID (DG Re-ID) benchmark.
  arXiv  Detail & Related papers  (2020-07-27T00:08:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.