Cross-Domain Transfer Learning

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14 papers in the last four weeks, up 133% on the four weeks before. 0.1% of all new papers.

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Latest papers 132

May 24, 2026cs.LG

Unifying Value Alignment and Assignment in Cross-Domain Offline Reinforcement Learning with Heterogeneous Datasets

Cross-domain offline reinforcement learning (RL) aims to learn a policy in the target domain with a limited target domain dataset and a source domain dataset that exhibits a dynamics shift. Training directly on the original source dataset typically leads to performance collapse. Recent studies perform data filtering from the perspective of dynamics alignment or value alignment to enable efficient policy transfer. However, these studies are typically validated on single-domain or single-behavior-policy source datasets. In this work, we explore a more general heterogeneous cross-domain offline RL setting, where the source datasets may be collected from multiple source domains by diverse behavior policies. We first uncover a critical yet overlooked issue in this setting: value misassignment. Empirically and theoretically, we demonstrate that value misassignment can undermine value alignment, mislead data filtering toward selecting suboptimal samples, and loosen the suboptimality gap, thereby degrading the agent's performance. To address this issue, we propose V2A, which integrates dynamics alignment, value alignment, and value assignment. V2A first employs temporally-consistent modality representation learning to extract dynamics modalities from the source dataset, followed by modality-aware advantage learning to rectify value alignment. Finally, it adopts a data filtering paradigm to selectively share source data for policy learning. Empirical results show that V2A significantly outperforms strong baseline methods under general heterogeneous cross-domain offline RL settings.

Zhongjian Qiao, Jiafei Lyu, Chenjia Bai +3

City University of Hong Kong · Tencent · Institute of Artificial Intelligence (TeleAI), China Telecom +1

Offline RL Cross-Domain Transfer Learning

May 22, 2026stat.AP

Distributionally Robust Transfer Learning with Structurally Missing Covariates, with Application to Cross-National Cardiac Arrest Prediction

Deploying clinical prediction models across healthcare systems often fails when key training covariates are unavailable at deployment and labeled outcomes are limited in the target domain. For example, high-performing models for out-of-hospital cardiac arrest (OHCA) rely on detailed prehospital measurements routinely collected in high-resource settings but unavailable in many international registries. Existing methods either discard missing covariates, sacrificing predictive information, or rely on untestable assumptions about their target distribution. We propose DRUM (\underline{D}istributionally \underline{R}obust \underline{U}nsupervised transfer learning with structurally \underline{M}issing covariates), a framework that transfers prediction models to target populations where certain covariates are structurally absent and outcome labels are unavailable. DRUM partitions covariates into shared components (

X

), observed across all settings, and missing components (

A

), observed only in the source. Rather than imputing missing covariates, DRUM optimizes worst-case predictive performance over the unknown target distribution of

A \mid X

using a neural network generator, with a robustness parameter controlling allowable deviation from the source conditional. We further develop a bias correction procedure that reduces sensitivity to nuisance estimation error. Simulations show substantial improvements in both mean and worst-case prediction error under distribution shift. Applied to cross-national OHCA prediction, transferring models from a US registry to multiple Asian registries where prehospital variables are unrecorded, DRUM yields better-calibrated predictions and improved clinical classification performance across sites.

Siqi Li, Chuan Hong, Ziye Tian +9

Centre for Biomedical Data Science, Duke-NUS Medical School, Singapore · 2Duke-NUS AI + Medical Sciences Initiative, Duke-NUS Medical School, Singapore · Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA +13

Healthcare Clinical Outcome Prediction Cross-Domain Transfer Learning Transfer Learning Clinical Risk Prediction

May 22, 2026cs.RO

Turning Adaptation into Assets: Cross-Domain Bridging for Online Vision-Language Navigation

Navigating under non-stationary environment shifts poses a critical challenge for a Vision-and-Language Navigation (VLN) agent deployed in the wild. Yet, existing Test-Time Adaptation (TTA) methods for VLN largely treat online adaptation as transient, isolated updates, leading to catastrophic forgetting and negative transfer. To overcome these issues, we propose Inter-Domain BridgE with Historical Assets (IDEA), a novel TTA framework that transforms adaptation into the accumulation and composition of assets. Specifically, IDEA introduces soft prompts optimized via a Fisher-guided weighting scheme to capture the transferable knowledge. These optimized prompts are then augmented with domain coordinates to form a dynamic asset library. Leveraging this library, IDEA constructs a cross-domain bridge by projecting the target domain onto the convex hull of historical knowledge. These designs form a complementary loop: the evolving library underpins bridge construction, while the bridge provides superior initialization to accelerate asset optimization. Extensive experiments across REVERIE, R2R, and R2R-CE benchmarks demonstrate the consistent superiority of IDEA over existing methods, showcasing its ability to enable training-free adaptation via asset sharing.

Zixuan Hu, Xuantuo Huang, Yancheng Li +3

School of Computer Science, Peking University, Beijing, China · School of Electronics, Peking University, Beijing, China · Peng Cheng Laboratory, Shenzhen, China

VLM Adaptation Continual Test-Time Adaptation Prompt Tuning Vision-Language Navigation Cross-Domain Transfer Learning

May 21, 2026cs.LG

RADAR: Relative Angular Divergence Across Representations

Machine learning methods rely on data. However, gathering suitable data can be challenging due to availability constraints, cost, or the need for domain expertise. Expanding datasets with additional sources is a common response to limited data, yet this practice does not always improve downstream performance and can sometimes lead to a loss of performance, known as negative transfer. We propose RADAR, a simple, geometrically grounded metric for estimating cross-domain transferability in foundation models. RADAR analyzes the layer-wise evolution of representations by measuring angular alignments and relative changes in distance along layer-to-layer displacement trajectories, and by comparing empirical distributions of within-domain and cross-domain dynamics. We hypothesize that domain transferability is related to the divergence between these trajectory distributions. We evaluate the metric across multiple modalities, including cross-lingual sentiment classification with text embedding models and cross-domain image classification with foundation vision models. Across several settings, RADAR provides competitive predictive performance relative to existing transferability metrics on several vision and text benchmarks, with particularly strong results when domain transitions are smooth or cleanly separated. Our ablations further suggest that the effectiveness of transferability estimation depends on the geometry of the model's internal representation space, with different modalities favoring different topological formulations.

Xavier Cadet, Mateusz Nowak, Peter Chin

Dartmouth College

Representation Geometry Task Transferability Estimation Cross-Domain Transfer Learning Transfer Learning

May 21, 2026cs.CV

Cross-Domain Human Action Recognition from Multiview Motion and Textual Descriptions

Robustness to domain changes is a key capability for effective deployment of human action recognition systems in real-world scenarios, where action categories at inference can present important domain shifts or even unseen actions from training. In this context, improving the recognition capabilities of Zero-Shot Action Recognition models (ZSAR), without requiring strong annotation efforts, remains a central challenge. Most ZSAR approaches assume that actions are observed under geometric conditions similar to those seen during training. In practice, variations in human body orientation and camera viewpoint add a significant domain gap in ZSAR, substantially limiting generalization to novel action-motion combinations. In this context, this paper presents a novel orientation-aware action recognition approach with improved cross-domain capabilities. Our approach combines motion cues of multiple camera viewpoints and text descriptions of human actions in the training phase. We present a new orientation-aware motion encoding network to learn different motion features, and adapt a specific orientation-aware text prompt to match the corresponding features at inference. Extensive experiments demonstrate that the proposed method consistently improves ZSAR performance across different recognition benchmarks, outperforming recent state-of-the-art zero-shot approaches on NTU-RGB+D, BABEL, NW-UCLA, and on two surveillance datasets. In addition, the learned representations exhibit strong transfer learning capabilities, yielding competitive performance on both cross-domain and same-domain recognition of seen actions. Code and trained models are available at: https://icb-vision-ai.github.io/OrientationAware-HAR

Yannick Porto, Renato Martins, Thomas Chalumeau +1

Université Bourgogne Europe, CNRS – France · TEB Group, Prynel SAS – France

Zero-Shot Learning Human Activity Recognition Video Action Recognition Cross-Domain Transfer Learning

May 21, 2026cs.LG

Target-Aligned Bellman Backup for Cross-domain Offline Reinforcement Learning

Cross-domain offline reinforcement learning (CDRL) aims to improve policy learning in a target domain by leveraging data collected from a source domain. Existing works typically assess the transferability of source-domain data by measuring its similarity to target-domain transitions, and implicitly perform transition-level selection. Transitions that are considered similar are assigned higher weights or rewards, while dissimilar ones are down-weighted. However, transition-level similarity does not necessarily imply consistency in long-term returns. Even visually or dynamically similar transitions may lead to significantly different outcomes in the target domain, which can mislead policy learning and degrade performance. To address this issue, we revisit the fundamental objective of policy learning. Since policy optimization ultimately relies on Bellman targets to evaluate the quality of decisions, we propose to assess the transferability of source-domain transitions based on their alignment with target-domain Bellman targets, rather than superficial transition similarity. Based on this insight, we propose a method termed Target-Aligned Bellman Backup (TABB), which selectively leverages source-domain data by measuring their contribution to accurate Bellman target estimation in the target domain. We evaluate TABB across a broad range of cross-domain offline RL settings with highly limited target-domain data. Experimental results show that TABB consistently achieves strong performance.

Wei Liu, Ting Long

School of Artificial Intelligence Jilin University China

Offline RL Cross-Domain Transfer Learning

May 18, 2026cs.LG

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs

Pre-training on text-attributed graphs (TAGs) is central to building transferable graph foundation models, where LLM-as-Aligner methods align graph and text representations through the semantic knowledge of large language models. However, these methods usually assume that node texts provide sufficient and reliable supervision, an assumption often violated in real-world sparse TAGs. When textual anchors are missing, noisy, or uneven across domains, graph structures must be aligned with weak semantic evidence, leading to unreliable structure-semantics correspondence and sparsity-induced transfer bias. This paper presents S2Aligner, a sparsity-aware and structure-enhanced LLM-as-Aligner framework for graph-text pre-training on sparse TAGs. The key idea is to decouple semantic alignment from structural modeling, allowing topology-aware signals to enhance alignment without contaminating the shared semantic space. Specifically, S2Aligner decomposes graph-text representations into semantic and structural components, uses structure-oriented reconstruction with consistency control to inject reliable topology cues into text representations, and suppresses inconsistent structural signals under textual sparsity. Moreover, S2Aligner introduces sparsity-aware cross-domain risk balancing, which calibrates domain risks through a global-domain density ratio and downweights unreliable sparse samples via graph reliability estimation. Theoretical analysis shows that this objective reduces cross-domain generalization gaps by controlling domain risk discrepancy. Extensive experiments across diverse graph domains, sparsity levels, and downstream tasks demonstrate that S2Aligner consistently outperforms existing baselines.

Yuhan Wang, Haopeng Zhang, Yibo Ding +6

Beihang University, Beijing, China · Tianjin University, Tianjin, China

Cross-Modal Alignment Text-Attributed Graph Learning Cross-Domain Transfer Learning Graph Foundation Models

May 18, 2026cs.RO

Transfer Learning for Customized Car Racing Environments

Transfer Learning, a technique where a model/agent can use the knowledge/expertise that it gained from one task and exploit that to solve another closely-related task, is often used in tackling problems in deep learning. Through this project, we explore transfer learning in the purview of deep reinforcement learning. Specifically, we want to use transfer learning to achieve the fast lap times in OpenAI's Car racing environment by training the agent on one circuit, and racing it on other customized target environments by zero-shot transfer or by additional fine-tuning. In addition, we compare the performance of model-based and model-free approaches, and observe that model-based approaches dominate in performance and converge faster than model-free approaches in this environment. We observe that transfer learning in most setups not only boosts the performance on the target domain, but also shows high performance ability during learning.

Benedict Florance Arockiaraj, Richard Chang, Wesley Yee

SEAS

Autonomous Racing Cross-Domain Transfer Learning Transfer Learning Model-Based RL

May 15, 2026cs.LG

CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts

Heterogeneous Graph Prompt Learning (HGPL)has emerged as a promising paradigm for bridging the gap between the objectives of pre-training foundation models and their downstream applications in heterogeneous graph settings. However, existing HGPL methods are primarily designed for in-domain scenarios, whereas real-world deployments often span multiple domains, and the data used for pre-training and downstream tasks may originate from different distributions. Consequently, the applicability of current HGPL approaches is limited to in-domain settings, and their performance typically degrades when application domains shift. To address this serious limitation, we develop CHoE, a cross-domain HGPL method built upon an expert network. During pre-training, we introduce and train structure-conditioned experts, and during prompt tuning, we adopt a structure-aware expert routing and load balancing mechanism to select structurally compatible experts for each meta-path view. In addition, we design a prompt-based semantic fusion module to integrate representations across multiple views for downstream prediction. Extensive experiments show that CHoE consistently improves performance in few-shot cross-domain applications, outperforming all baseline approaches.

Peiyuan Li, Yongqi Huang, Jitao Zhao +3

School of Computer Science and Technology, Tianjin University, Tianjin, China · Department of Health Technology and Informatics, and Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Kowloon, Hong Kong

Heterogeneous GNNs Few-Shot Learning Cross-Domain Transfer Learning Mixture-of-Experts Models

May 13, 2026cs.LG

Decoupled and Divergence-Conditioned Prompt for Multi-domain Dynamic Graph Foundation Models

Dynamic graphs are ubiquitous in real-world systems, and building generalizable dynamic Graph Foundation Models has become a frontier in graph learning. However, dynamic graphs from different domains pose fundamental challenges to unified modeling, as their semantic and temporal patterns are inherently inconsistent, making the multi-domain pre-training difficult. Consequently, the widely used "pretrain-then-finetune" paradigm often suffers from severe negative knowledge transfer. To the best of our knowledge, there exists no multi-domain dynamic GFM. In this work, we propose DyGFM, a Dynamic Graph Foundation Model over multiple domains based on decoupled and divergence-conditioned prompting. To disentangle transferable semantics from the domain-specific dynamics, we introduce a dual-branch pre-training strategy with semantic-temporal decoupling. To alleviate negative transfer during domain adaptation, we further develop a cross-domain routing mechanism with divergence-aware expert selection. To enable efficient downstream fine-tuning, we design a divergence-conditioned prompt generator that injects lightweight, learnable graph prompts tailored to semantic and temporal traits. Extensive experiments on continuous dynamic graph benchmarks demonstrate that DyGFM consistently outperforms 12 state-of-the-art baselines on both node classification and link prediction tasks, achieving superior effectiveness and efficiency.

Haonan Yuan, Qingyun Sun, Junhua Shi +3

Domain-Adaptive Pretraining Prompt Tuning Cross-Domain Transfer Learning Dynamic Graph Learning Graph Foundation Models

May 13, 2026cs.CV

Neural Video Compression with Domain Transfer

Content-adaptive compression has always been a key direction in neural video coding (NVC), aiming to mitigate the domain gap between training and testing data. Such gaps often arise from distributional discrepancies between training and inference data, which may cause noticeable performance degradation when the testing content differs from the training distribution. To tackle this challenge, we propose DCVC-DT, a domain transfer enhanced neural video compression framework. Specifically, we design a lightweight online domain transfer (DT) mechanism that dynamically adapts the encoded latent representation during inference, effectively bridging the domain gap without modifying the encoder or decoder parameters. In addition, we develop a frame-level dynamic RD (Rate and Distortion) adjustment scheme that actively regulates the ratio of R and D in the loss function based on quality fluctuation, thereby improving rate-distortion performance. Extensive experiments demonstrate that DCVC-DT achieves up to 6.21% bitrate savings over the baseline DCVC-DC, while significantly enhancing generalization to unseen testing data and alleviating error propagation. Our code is available at https://github.com/SunnyMass/DCVC-DT.

Tiange Zhang, Rongqun Lin, Xiandong Meng +4

Shenzhen Graduate School, Peking University, Shenzhen, China · Pengcheng Laboratory, Shenzhen, China · School of Computer Science, Peking University, Beijing, China

Test-Time Adaptation Cross-Domain Transfer Learning Rate-Distortion Optimization Neural Video Compression Video Compression Neural Network Compression

May 13, 2026cs.LG

Generative Support Realignment for Cross-Domain Offline Reinforcement Learning

Cross-domain offline reinforcement learning learns a target policy from pre-collected source and target datasets with different dynamics. When target data are scarce, effectively compensating for their limited coverage using source data remains challenging due to the discrepancy between domains. We propose Target-aligned Coverage Expansion (TCE), which leverages source states to generatively realign and expand the limited target support while controlling the generation error induced by this expansion. We further derive a performance gap bound that characterizes the interplay between generation error and source--target dynamics gap, providing theoretical guidance for effective coverage expansion and source utilization. Across diverse cross-domain environments, TCE consistently outperforms state-of-the-art baselines, while further analysis empirically supports the guidance provided by our theoretical findings.

Minung Kim, Jeongmo Kim, Gwanwoo Choi +1

Graduate School of Artificial Intelligence Ulsan National Institute of Science and Technology (UNIST)

Offline RL Cross-Domain Transfer Learning

May 12, 2026cs.CV

A Transfer Learning Evaluation of Deep Neural Networks for Image Classification

Transfer learning is a machine learning technique that uses previously acquired knowledge from a source domain to enhance learning in a target domain by reusing learned weights. This technique is ubiquitous because of its great advantages in achieving high performance while saving training time, memory, and effort in network design. In this paper, we investigate how to select the best pre-trained model that meets the target domain requirements for image classification tasks. In our study, we refined the output layers and general network parameters to apply the knowledge of eleven image processing models, pre-trained on ImageNet, to five different target domain datasets. We measured the accuracy, accuracy density, training time, and model size to evaluate the pre-trained models both in training sessions in one episode and with ten episodes.

Nermeen Abou Baker, Nico Zengeler, Uwe Handmann

Computer Science Institute, Ruhr West University of Applied Sciences, 46236 Bottrop, Germany

Fine-Tuning Cross-Domain Transfer Learning Transfer Learning Image Classification

May 11, 2026cs.AI

Interpretable Machine Learning for Football Performance Analysis: Evidence of Limited Transferability from Elite Leagues to University Competition

Machine learning has become increasingly prevalent in football performance analysis, yet most studies prioritize predictive accuracy while implicitly assuming that learned performance determinants and their interpretations are transferable across competition levels. Whether interpretability remains reliable under domain shift-from elite to university football remains largely unexplored. This study investigates whether performance determinants learned from elite competitions are structurally transferable to university-level football and whether their interpretations remain robust under domain shift. Models were trained on large-scale event data from the top five European leagues and applied to university football data from National Tsing Hua University (NTHU) using an identical feature space. Random Forest and Multilayer Perceptron models were interpreted using SHapley Additive exPlanations (SHAP) and Counterfactual Impact Score (CIS). Across five experiments, elite football exhibited a stable and consistent hierarchy of performance determinants across leagues, models, and explanation methods. In contrast, NTHU university football showed substantial reordering of key indicators, reduced explanation stability, weaker structural agreement with elite domains, and increased sensitivity to explanation method. These findings suggest that interpretability robustness is domain-dependent. Rather than reflecting methodological limitations alone, instability in explanations under domain shift may serve as a diagnostic signal of structural ambiguity in the target domain.

Yu-Fang Tsai, Yu-Jen Chen, Kok-Hua Tan +5

Department of Kinesiology, National Tsing Hua University, Hsinchu, Taiwan · Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan · Physical Education Office, National Tsing Hua University, Hsinchu, Taiwan +4

Distribution Shift Robustness SHAP Feature Attribution Cross-Domain Transfer Learning Explainability Evaluation

May 10, 2026physics.plasm-ph

TokaMind for Power Grid: Cross-Domain Transfer from Fusion Plasma

TokaMind is a multi-modal transformer (MMT) foundation model pre-trained on tokamak plasma diagnostics data from MAST, where it was shown to outperform CNN-based approaches on fusion benchmarks. We investigate whether its learned representations generalize to physically distinct but structurally analogous domains. Through systematic experimentation across four domains-industrial bearing degradation, NASA CMAPSS turbofan degradation, and two independent power grid PMU datasets-we identify four transfer-favoring characteristics that help explain where TokaMind's pretrained representations are most effective. Power grid synchrophasor data matches this target-domain profile most directly, while industrial degradation datasets demonstrate that TokaMind can still yield useful performance under partial alignment, especially when task design and feature construction expose physically meaningful degradation structure. On the GESL/PNNL 500-event benchmark with provider-aware evaluation, TokaMind achieves test

\text{F1} = 0.837 \pm 0.040

(3seeds) for severe event classification. Our central finding, however, is not the aggregate score: classification difficulty is structurally determined by provider-level grid topology, not model capacity. In the single-window early-warning regime, TokaMind outperforms a CNN baseline (F10.889 vs.~0.878)--a reversal that disappears as more event windows are provided. Furthermore, Critical Slowing Down (CSD) indicators, used as a confidence gate rather than a classification label, improve F1 from 0.696 to 0.750 at 63% coverage-outperforming the CNN baseline (0.636) at any coverage level. These results establish the first cross-domain validation of TokaMind outside nuclear fusion and propose a transferability framework and revised evaluation protocol for multi-source PMU datasets.

JC Wu, Norton Lee, Kai Siang Chen

TaiScience Research Group, affiliated with Fu Jen Catholic University, Taiwan · Center for Geometry and Physics, Institute for Basic Science (IBS), South Korea · Fu Jen Catholic University, Taiwan

Time Series Classification Cross-Domain Transfer Learning Time-Series Foundation Models Cross-Dataset Generalization Power Systems

May 9, 2026cs.LG

FactoryNet: A Large-Scale Dataset toward Industrial Time-Series Foundation Models

We introduce the first universal pretraining corpus for industrial time-series data: FactoryNet. 51M datapoints across 23k end-to-end task executions (13.3k real, 9.8k synthetic) on six embodiments, unified by a shared schema that enables robust zero-shot cross-embodiment transfer and highly parameter-efficient anomaly detection. We introduce a novel schema: Setpoint, Effort, Feedback, Context (S-E-F-C) underlying the whole pipeline that maps any actuated system into a common representational frame. The corpus spans 27 annotated anomaly types alongside healthy baselines and counterfactual pairs across robotic manipulation and machining domains. Cross-embodiment transfer experiments yield positive results: under bias-aware metrics our model demonstrates fair cross-embodiment transfer capabilities on the evaluated source-target pair, while 24 schema-aligned signals achieves competitive anomaly detection performance compared to high-dimensional baselines. We release FactoryNet as a growing, multi-embodiment dataset to drive progress toward industrial foundation models.

Karim Othman, Jonas Petersen, Matei Ignuta-Ciuncanu +5

1Cairo University · 2Forgis · 3ETH Zurich +3

Manufacturing Industrial Anomaly Detection Cross-Domain Transfer Learning Time-Series Anomaly Detection Time-Series Foundation Models

May 8, 2026cs.LG

Transfer Learning Across Fast- and Full-Simulation Domains in High-Energy Physics

Machine-learning models in high-energy physics are often trained on simulated data, where fully simulated samples are computationally expensive while fast simulation provides large statistics at reduced realism. In this work, we systematically study transfer learning between fast-simulated and fully simulated datasets in a realistic LHC environment. We consider three representative tasks, signal-background classification, quark-gluon jet tagging, and missing transverse energy reconstruction, using dense neural networks, graph neural networks, and transformer-based architectures. Models are pretrained on ATLAS-like fast simulation and adapted to CMS-like fast simulation and to fully simulated ATLAS Open Data. Across all tasks, pretrained models consistently outperform independently trained baselines and require significantly less target-domain training data, typically reducing the needed statistics by about a factor of two. These results demonstrate that fast simulation can be used to learn robust, reusable representations and motivate publishing trained models as reusable scientific assets beyond large foundation models.

Matthias Schott, Lucie Flek

Institute of Physics, University of Bonn, Germany · Bonn-Aachen International Center for Information Technology (b-it), University of Bonn, Germany

Domain Adaptation High-Energy Physics Cross-Domain Transfer Learning Transfer Learning Data-Efficient Learning

May 7, 2026cs.CV

Knowledge Transfer Scaling Laws for 3D Medical Imaging

Vision foundation models are increasingly moving beyond 2D to volumetric domains such as 3D medical imaging, where unified pretraining across different imaging modalities (i.e. CT, MRI, and PET) could provide foundational models for diverse clinical tasks. However, training such models requires mixing heterogeneous imaging domains, and current mixture strategies remain largely heuristic. In this work, we observe that different medical imaging domains scale at variable rates during pretraining, and knowledge transfer between domains is strongly asymmetric: training on one domain can substantially improve another, but the reverse may be much weaker. Interestingly, both MAE reconstruction loss and cross-domain transfer follow predictable power-law trends with domain-specific behaviors. Motivated by these findings, we formulate data allocation as a scaling-law optimization problem. The derived allocations reveal an interpretable hub-and-island structure: highly transferable domains emerge as hubs that benefit many others and deserve strategic allocation, while isolated domains act as islands requiring direct investment. Empirically, transfer-aware allocation outperforms data-proportional sampling by up to 58% and generalizes well to unseen budgets with r=0.989. Downstream validation on disease classification and organ/lesion segmentation further confirms that the derived transfer-aware mixtures provide stronger pretrained representations for clinical 3D medical imaging tasks.

Ho Hin Lee, Dongna Du, Chu Wang +4

Vanderbilt University · Zhejiang University · McGill University +1

Medical Imaging Foundation Models 3D Medical Imaging Multimodal Pretraining Cross-Domain Transfer Learning Scaling Laws

May 7, 2026cs.LG

Scene-Adaptive Continual Learning for CSI-based Human Activity Recognition with Mixture of Experts

Channel state information (CSI)-based human activity recognition (HAR) is vulnerable to performance degradation under domain shifts across varying physical environments. Continual learning (CL) offers a principled way to learn new domains sequentially while preserving past knowledge, but existing CL solutions for CSI-based HAR scale poorly with accumulating domains, rely on a large replay buffer, or incur linearly growing inference cost. In this letter, we propose Scene-Adaptive Mixture of Experts with Clustered Specialists (SAMoE-C), which formulates cross-domain CSI-based HAR as a mixture-of-experts system that enables scene-specific adaptation, via an attention-based semantic router that activates only selected experts for each input. Moreover, we develop a novel training protocol, which requires only a tiny replay buffer for stabilizing domain discrimination of the router. Experimental results on a four-scene CSI dataset demonstrate that SAMoE-C approaches the state-of-the-art accuracy, while maintaining a significantly lower inference cost. By jointly combining modular experts, selective activation with router and a lightweight training protocol, SAMoE-C enables scalable cross-domain CSI-based HAR deployment with low training overhead and high computational efficiency in real-world settings.

Wenhan Zheng, Yuyi Mao, Ivan Wang-Hei Ho

Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong · School of Computer Science and Engineering, The Macau University of Science and Technology, Macau

Adaptive Inference Human Activity Recognition Continual Learning Cross-Domain Transfer Learning Mixture-of-Experts Models

May 6, 2026cs.LG

YOTOnet: Zero-Shot Cross-Domain Fault Diagnosis via Domain-Conditioned Mixture of Experts

Mechanical equipment forms the critical backbone of modern industrial production, yet domain shift severely limits the generalization of deep learning based fault diagnosis models across different equipment and operating conditions.Inspired by the success of foundation models in achieving zero-shotgeneralization, we propose YOTOnet (You Only Train Once), a novel architecture specifically designed for cross-domain fault diagnosis in mechanical equipment.YOTOnet comprises three core components: (1) a physics-aware Invariant Feature Distiller that extracts domain-agnostic representations using multi-scale dilated convolutions and FFT-based time-frequency fusion,(2) Domain-Conditioned Sparse Experts (DC-MoE) that adaptively route inputs to specialized processors via learned gating without external meta-data, and (3) a dual-head classification system with auxiliary supervision.Extensive validation on five public bearing datasets (CWRU, MFPT, XJTU,OTTAWA, HUST) through 30 cross-dataset protocols demonstrates the superiority of YOTOnet compared with other state-of-the-art methods. Critically, we observe a clear scaling effect-average test F1 improves from 0.5339(1 training dataset) to 0.705 (4 datasets), with a clear gain when moving from 3 to 4 datasets. These findings provide empirical evidence that foundation model principles can enable robust, train-once deployment for industrial fault diagnosis.

Zesen Wang, Zihao Wu, Yue Hu +2

School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, China

Cross-Domain Transfer Learning Bearing Fault Diagnosis Mixture-of-Experts Models Cross-Dataset Generalization Fault Diagnosis

May 5, 2026cs.CL

SAM-NER: Semantic Archetype Mediation for Zero-Shot Named Entity Recognition

Zero-shot Named Entity Recognition (ZS-NER) remains brittle under domain and schema shifts, where unseen label definitions often misalign with a large language model's (LLM's) intrinsic semantic organization. As a result, directly mapping entity mentions to fine-grained target labels can induce systematic semantic drift, especially when target schemas are novel or semantically overlapping. We propose \textbf{SAM-NER}, a three-stage framework based on \emph{Semantic Archetype Mediation} that stabilizes cross-domain transfer through an intermediate, domain-invariant archetype space. SAM-NER: (i) performs \emph{Entity Discovery} via cooperative extraction and consensus-based denoising to obtain high-coverage, high-fidelity entity spans; (ii) conducts \emph{Abstract Mediation} by projecting entities into a compact set of universal semantic archetypes distilled from high-level ontological abstractions; and (iii) applies \emph{Semantic Calibration} to resolve archetype-level predictions into target-domain types through constrained, definition-aligned inference with a frozen LLM. Experiments on the CrossNER benchmark show that SAM-NER consistently outperforms strong prior ZS-NER baselines in cross-domain settings. Our implementation will be open-sourced at https://github.com/DMIRLAB-Group/SAM-NER.

Ruichu Cai, Juntao Gan, Miao Mai +2

School of Computer Science, Guangdong University of Technology · Peng Cheng Laboratory · Nanfang Media Group(Nanfang Daily) +1

Named Entity Recognition Cross-Domain Transfer Learning LLM Information Extraction

May 4, 2026cs.CV

One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation

Deep learning-based medical image segmentation is increasingly used to support clinical diagnosis and develop new treatment strategies. However, model performance remains limited by the scarcity of high-quality annotated data and insufficient generalization across imaging protocols. This limitation is particularly evident in MRI and CT, where models are typically trained on a single acquisition sequence and exhibit reduced robustness when applied to unseen sequences or contrasts. Although data augmentation is widely used to improve general robustness on medical images, its impact on cross-modality generalization has not been quantitatively explored. In this work, we study a targeted set of data augmentation techniques designed to improve cross-modality transfer. We train three spine segmentation models, each on a single-modality/sequence dataset, and evaluate them across seven out-of-distribution datasets (spanning CT and MRI), reflecting a realistic single-sequence training and multi-sequence/contrast/modality deployment scenario. Our results demonstrate substantial performance gains on unseen domains (average Dice gain of 155 %) while preserving in-domain accuracy (average Dice decrease of 0.008 %), including effective transfer between CT and MRI. To mitigate the computational cost typically associated with strong data augmentation, we implement GPU-optimized augmentations that maintain, and even improve, training efficiency by approximately 10 %. We release our approach as an open-source toolbox, enabling seamless integration into commonly used frameworks such as nnUNet and MONAI. These augmentations significantly enhance robustness to heterogeneous clinical imaging scenarios without compromising training speed.

Nathan Molinier, Hendrik Möller, Thomas Dagonneau +6

NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal · Mila – Quebec AI Institute · Department for Interventional and Diagnostic Neuroradiology, TUM UniversityMay Hospital +3

Cross-Modality Medical Image Segmentation Data Augmentation Cross-Domain Transfer Learning Medical Image Segmentation 3D Medical Image Segmentation

May 4, 2026cs.LG

Pretraining on Sleep Data Improves non-Sleep Biosignal Tasks

Sleep foundation models have recently demonstrated strong performance on in-domain polysomnography tasks, including sleep staging, apnea detection, and disease risk prediction. In this work, we investigate whether sleep biosignals can serve as an effective pretraining distribution for learning representations that transfer beyond sleep to adjacent domains. Following sleep foundation models, we perform sleep-only multimodal contrastive pretraining (with a leave-one-out objective) and evaluate transfer to non-sleep EEG and ECG, two well-benchmarked biosignal modalities with heterogeneous datasets and clinically meaningful downstream tasks. Across eight downstream tasks spanning multiple EEG and ECG datasets, sleep pretraining consistently improves performance relative to training from scratch. Moreover, on several tasks, we achieve performance competitive with or surpassing prior specialized state-of-the-art and foundation models.

William Lehn-Schiøler, Magnus Ruud Kjær, Phillip Hempel +7

BrainCapture ApS, Kongens Lyngby, Denmark · DTU Health Tech, Technical University of Denmark, Kongens Lyngby, Denmark · DTU Compute, Technical University of Denmark, Kongens Lyngby, Denmark +4

Self-Supervised Time Series Representation Learning Cross-Domain Transfer Learning

May 3, 2026cond-mat.mtrl-sci

Meta-LegNet: A Transferable and Interpretable Framework for Surface Adsorption Prediction via Self-Defined Adsorption-Environment Learning

A central challenge in computational catalysis is the identification of low-energy and chemically plausible adsorption configurations, as these directly affect adsorption energies, reaction pathways, and catalytic performance. Existing approaches generally rely on enumerating candidate adsorption sites followed by iterative refinement through density functional theory calculations or machine-learning-based relaxations. However, such workflows remain computationally expensive and are difficult to scale to complex surfaces or multi-adsorbate systems. Here, we introduce Meta-LegNet, a graph learning framework that combines SE(3)-equivariant atom-level message passing with voxel-based multiscale aggregation and cross-domain meta-learning to learn transferable representations of local adsorption environments across diverse catalyst--adsorbate systems. Rather than following a conventional regression-only paradigm, Meta-LegNet encodes local chemical environments using invariant radial features and equivariant directional information, and further incorporates broader structural context through coordinate-frame voxel pooling, assignment-based upsampling, and gated feature fusion. The resulting local-global decomposition produces atom-resolved attribution maps, which are processed to identify adsorption-relevant local environments in an interpretable manner. Based on the learned representations, we further construct an adsorption-environment database and develop a template-matching strategy to propose likely adsorption sites on previously unexplored surfaces without exhaustive site enumeration. Overall, our results suggest that learning transferable adsorption environments provides an accurate, interpretable, and practical route for accelerating catalyst screening.

Yifan Li, Arravind Subramanian, Xiaoqing Liu +3

Department of Mechanical Engineering,National University of Singapore, 117575, SG · Department of Chemical and Biomolecular Engineering,National University of Singapore, 117575, SG

Geometric Representation Learning Cross-Domain Transfer Learning Computational Materials Science

May 2, 2026cs.CV

Multi-Dataset Cross-Domain Knowledge Distillation for Unified Medical Image Segmentation, Classification, and Detection

We propose a unified cross-domain transfer learning framework that leverages knowledge from multiple heterogeneous medical imaging datasets to improve performance across segmentation, classification, and object detection tasks. Our approach employs a teacher-student paradigm in which a joint teacher model aggregates domain-invariant representations learned from diverse source datasets, while a task-specific student model is trained via multi-level knowledge distillation. Originally developed for medical image segmentation, the framework is extended to support image-level classification and object-level detection, enabling a general multi-task formulation for medical image analysis. We evaluate our method on a broad suite of datasets, including six segmentation benchmarks, BrainMetShare, ISLES, BraTS (MRI) and Lung MSD, LiTS, KiTS (CT), as well as multiple classification datasets for pulmonary disease and dementia, and detection datasets with native bounding-box annotations. Across all tasks and modalities, the proposed approach yields consistent improvements over strong dataset-specific and multi-head baselines, demonstrating enhanced robustness to distributional shifts and superior generalization. These findings highlight the potential of multi-dataset knowledge distillation as a scalable and task-agnostic approach for enhancing segmentation, classification, and object detection performance across heterogeneous medical imaging domains.

Ceausescu Ciprian-Mihai, Anghelina Ion-Marian, Alexe Dumitru-Bogdan

Faculty of Mathematics and Computer Science, University of Bucharest, Str. Academiei, 14, Bucharest, 010014, Romania · Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy, Romania, Calea 13 Septembrie, 13, Bucharest, 050711, Romania

Multi-Task Learning Medical Image Classification Cross-Domain Transfer Learning Medical Image Segmentation Object Detection Knowledge Distillation

May 1, 2026cs.SI

Empowering Heterogeneous Graph Foundation Models via Decoupled Relation Alignment

While Graph Foundation Models (GFMs) have achieved remarkable success in homogeneous graphs, extending them to multi-domain heterogeneous graphs (MDHGs) remains a formidable challenge due to cross-type feature shifts and intra-domain relation gaps. Existing global feature alignment methods (PCA or SVD) enforce a shared feature space blindly, which distorts type-specific semantics and disrupts original topologies, inevitably leading to "Type Collapse" and "Relation Confusion". To address these fundamental limitations, we propose Decoupled relation Subspace Alignment (DRSA), a novel, plug-and-play relation-driven alignment framework. DRSA fundamentally shifts the paradigm by decoupling feature semantics from relation structures. Specifically, it introduces a dual-relation subspace projection mechanism to coordinate cross-type interactions within a shared low-rank relation subspace explicitly. Furthermore, a feature-structure decoupled representation is designed to decompose aligned features into a semantic projection component and a structural residual term, adaptively absorbing intra-domain variations. Optimized via a stable alternating minimization strategy based on Block Coordinate Descent, DRSA constructs a well-calibrated, structure-aware latent space. Extensive experiments on multiple real-world benchmark datasets demonstrate that DRSA can be seamlessly integrated as a universal preprocessing module, significantly and consistently enhancing the cross-domain and few-shot knowledge transfer capabilities of state-of-the-art GFMs. The code is available at: https://github.com/zhengziyu77/DSRA.

Ziyu Zheng, Yaming Yang, Zhe Wang +2

School of Computer Science and Technology, Xidian University, Xi’an, China 710071

Heterogeneous GNNs Cross-Domain Transfer Learning Graph Foundation Models

May 1, 2026cs.LG

Label-Conditioned Cross-Modal Fusion for Adult-to-Pediatric ECG Transfer via Curriculum-Gated Contrastive Alignment

Automated pediatric electrocardiogram (ECG) interpretation remains challenging because developmental differences in heart rate, intervals, and waveforms limit the transferability of models trained mainly on adult data, while expert-labeled pediatric ECG cohorts are scarce. We propose PEACE (Pediatric-Adult ECG Alignment via Cross-modal Enhancement), an adult-to-pediatric ECG transfer framework pretrained on MIMIC-IV ECGs and adapted to pediatric targets. PEACE integrates label-specific bidirectional contrastive learning (LSBC) to align ECG representations with diagnostic semantics and curriculum adaptive fusion (CAF) to stabilize optimization under limited pediatric supervision. Label-conditioned short text descriptors provide auxiliary semantic supervision during training, whereas inference requires ECG signals only. On ZZU-pECG, PEACE achieves macro-average AUCs of 59.39%, 81.74%, and 91.56% under zero-shot, 50-shot, and full fine-tuning settings, respectively, outperforming ECG-only, multimodal, and generic domain adaptation baselines including DANN and MMD. On PTB-XL, it reaches 96.90% macro-average AUC after full fine-tuning over nine harmonized labels with nonzero mapped incidence. Gradient-based attention maps show increased saliency around QRS voltage and morphology regions for chamber-related RVH and around QRS-to-T/repolarization intervals for LQTS, broadly consistent with ECG regions commonly inspected during routine interpretation. These results suggest that adult-scale ECG pretraining coupled with rhythm, morphology, and ST-T repolarization semantic descriptors improves transferable pediatric diagnosis under label scarcity while preserving clinically interpretable waveform focus.

Xinran Liu, Yuwen Li, Hongxiang Gao +4

School of Instrument Science and Engineering, Southeast University, Nanjing 210096, Nanjing, China · Nanjing Medical University, Nanjing 211166, Nanjing, China · Zhengzhou University, Zhengzhou 450001, Zhengzhou, China

Electrocardiography Cross-Modal Alignment Electrocardiogram Classification Cross-Domain Transfer Learning Multimodal Contrastive Learning

Apr 29, 2026cs.LG

Advancing multi-site emission control: A physics-informed transfer learning framework with mixture of experts for carbon-pollutant synergy

Municipal solid waste incineration (MSWI) converts urban waste to energy but simultaneously emits carbon dioxide, carbon monoxide and multiple regulated air pollutants whose formation is tightly coupled within a single combustion system. Controlling these emissions across a network of diverse facilities poses a fundamentally different challenge from optimising a single plant: data-driven models trained at one site capture local statistical patterns that rarely survive transfer to another, because they lack the physical constraints and regime-level structure needed to generalise. Here we show that shared emission-control relationships can be identified across heterogeneous MSWI plants when physical conservation laws, operating-regime heterogeneity and carbon-pollutant coupling are treated jointly. We develop a carbon-pollutant mixture-of-experts (CPMoE) model that routes process observations through regime-specific expert networks under conservation-based regularisation, and combine it with physics-informed transfer learning to adapt a reference model to new facilities. Across 13 plants, CPMoE predicts six major pollutants and a composite system-level risk index with source-domain R2 of 0.668-0.904 and 0.666-0.970, respectively; after transfer to 12 target plants these values remain 0.661-0.842 and 0.610-0.841. Expert-utilisation patterns show that adaptation proceeds through structured regime re-weighting rather than re-learning from scratch. Embedding the transferred model in an offline digital twin and screening candidate operating adjustments against historical process records yields consistent risk-index reductions of 3.6-6.3% with simultaneous pollutant co-reductions in 94-100% of evaluated samples. These findings suggest a practical route toward transferable, system-level decision support for carbon-pollutant co-control in heterogeneous waste-to-energy networks.

Yuxuan Ying, Hanqing Yang, Kaige Wang +6

College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, China. · Alibaba Group, Hangzhou, 311100, China. · School of Computer Science and Technology, Zhejiang Normal University, Hangzhou, 311231, China. +2

Cross-Domain Transfer Learning Physics-Informed ML Mixture-of-Experts Models

Apr 29, 2026cs.CV

Cross-Domain Transfer of Hyperspectral Foundation Models

Hyperspectral imaging (HSI) semantic segmentation typically relies on in-domain training, but limited data availability often restricts model performance in real-world applications. Current approaches to leverage foundation models in proximal sensing use cross-modality techniques, bridging RGB and HSI to exploit vision foundation models. However, these methods either discard spectral information or introduce architectural complexity. We propose cross-domain transfer as an alternative, reusing HSI foundation models - originally trained in remote sensing - for proximal sensing applications. By eliminating the need to bridge modality gaps, our approach preserves spectral information while maintaining a simple architecture. Using the HS3-Bench benchmark, we systematically evaluate and compare conventional in-domain, in-modality training, cross-modality transfer and cross-domain transfer strategies. Our results demonstrate that cross-domain transfer achieves large performance improvements over in-domain, in-modality training, reduces the performance gap to cross-modality approaches and maintains strong performance in limited data settings. Thus, this work advances more effective HSI semantic segmentation in diverse applications.

Nick Theisen, Peer Neubert

Intelligent Autonomous Systems, University of Koblenz, Germany

Remote Sensing Image Segmentation Hyperspectral Imaging Cross-Domain Transfer Learning

Apr 28, 2026cs.CR

Medoid Prototype Alignment for Cross-Plant Unknown Attack Detection in Industrial Control Systems

Deploying an intrusion detector trained in one industrial plant to another remains difficult because Industrial Control System (ICS) traffic is highly site-dependent, labels are scarce, and unseen attacks often appear after deployment. To address this challenge, this paper introduces a medoid prototype alignment framework for cross-plant unknown attack detection. Instead of aligning all source and target samples directly, the method first compresses heterogeneous traffic into a comparable representation space and then extracts robust medoid prototypes that summarize local operational structure in each domain. A prototype-calibrated transfer objective is further designed to align target prototypes with source prototypes while preserving source-domain discrimination and encouraging confident target predictions. This strategy reduces noisy cross-domain matching and improves transfer stability under heterogeneous industrial conditions. Experiments conducted on natural gas and water storage control systems show that the proposed method achieves the best average performance among all compared models, reaching an average accuracy of 0.843 and an average F1-score of 0.838 across four unknown-attack transfer tasks. The analysis also shows clear transfer asymmetry between source-target directions and confirms that prototype guidance is especially helpful on challenging reverse-transfer settings. These findings suggest that medoid prototype alignment is a practical solution for robust industrial intrusion detection under domain shift.

Luyao Wang

University of Malaya Malaysia

Domain Adaptation Network Intrusion Detection Cross-Domain Transfer Learning