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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Continuous Cross-Domain Traffic State Prediction via Memory-Augmented Graph Liquid Time-Constant Networks
Jun 14, 2026Jinrong Xiang, Ming XuCross-Domain Transfer Learning Spatiotemporal Forecasting
Software College, Liaoning Technical University, Huludao, Liaoning 125100, China
CIWI-CKT: Chaos-Informed Wave Interference Feature Fusion and Cross-City Knowledge Transfer for Traffic Flow Forecasting
Jun 14, 2026Abdul Joseph Fofanah, Lian Wen, David Chen +1Meta-Learning Few-Shot Learning
School of Information and Communication Technology, Griffith University, Brisbane, 4111, Australia · School of Information Engineering, Chang’an University, Xi’an, China
Polyp-D2ATL: Deep Domain-Adaptive Transfer Learning for Colorectal Polyp Classification under Label Distribution Shift
Jun 12, 2026Sajad Jabarzadeh Ghandilu, Maryam Sadat Hosseini Azad, Shahriar Baradaran Shokouhi +1Class-Imbalanced Learning Label Shift
School of Electrical Engineering, Sharif University of Technology, Tehran, Iran · School of Electrical Engineering, Iran University of Science and Technology,2026 Tehran, Iran
GRASP: Gradient-Aligned Sequential Parameter Transfer for Memory-Efficient Multi-Source Learning
Jun 12, 2026Mary Isabelle Wisell, Nicholas Jacobs, Aayush Manandhar +1Domain-Incremental Learning Cross-Domain Transfer Learning
San Diego State University, San Diego, CA, USA · University of Utah, Salt Lake City, UT, USA · University of Maine, Orono, ME, USA
MoE Enhanced Federated Learning for Spatiotemporal Prediction
Jun 9, 2026Zhehao Dai, Xiao Han, Zhaolin Deng +4Intelligent Transportation Systems Mixture of Experts
Zhejiang University of Technology, Zhejiang Key Laboratory of Visual Information Intelligent Processing, Hangzhou · Jilin University, Jilin · City University of Hong Kong, Hong Kong SAR China
A Source Domain is All You Need: Source-Only Cross-OS Transfer Learning for APT Anomaly Detection via Semantic Alignment and Optimal Transport
Jun 8, 2026Sidahmed Benabderrahmanea, Petko Valtchev, James Cheney +1Cross-Domain Transfer Learning Optimal Transport
New York University, NYUAD, Division of Science, Computer Science Department. · University of Quebec in Montreal, Computer Science Department, Montreal. · University of Edinburgh, School of Informatics, Edinburgh.
Hierarchical Projection for Adaptive Knowledge Transfer
Jun 7, 2026Samhita Pal, Tian GuHierarchical Bayesian Modeling Cross-Domain Transfer Learning
Vanderbilt University Medical Center · Columbia University
Have I Solved This Before? Retrieving Similar Segmentation Problems for Evolutionary Learning
Jun 6, 2026Andreas Margraf, Henning Cui, Jörg HähnerManufacturing Image Segmentation
Institute of Computer Science, University of Augsburg, Germany
The Cross-Architecture Substrate: A Domain-Transcendent, Calibration-Surviving Geometric Invariant of Modern Vision Encoders
Jun 5, 2026Yousef RadwanTask Transferability Estimation Cross-Domain Transfer Learning
KAUST
Transfer learning for causal forest
Jun 5, 2026Bérénice-Alexia Jocteur, Véronique Maume-Deschamps, Pierre RibereauCausal Effect Estimation Conditional Average Treatment Effect Estimation
ICJ, PSPM · Universit´e Lyon 1, Centrale Lyon, INSA Lyon · Universit´e Jean Monnet, CNRS, ICJ UMR5208, 69622 Villeurbanne, France. +1
Simultaneous hyperkinetic movement disorders phenotyping: a cross-cohort pediatric transfer study using routine videos, markerless pose estimation and a tabular foundation model
Jun 4, 2026Laura Cif, Diane Demailly, Zohra Souei +11Cross-Domain Transfer Learning
Service of Neurology, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland · Institut du Neurone, Montferrier sur Lez, France · Department of Neurology, Clinique Beau Soleil, Institut Mutualiste Montpelliérain, Montpellier, France +10
Harnessing Structural Context for Entity Alignment Foundation Models
Jun 4, 2026Xingyu Chen, Yuanning Cui, Zequn Sun +1KG Embedding Cross-Domain Transfer Learning
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China · Nanjing University of Information Science and Technology, Nanjing, China · National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
A Novel Method with Encoder-Decoder for Cross-Sensor Adaptation in Surface Shape Sensing with Sparse Strain Sensors
Jun 4, 2026Shuo Wang, Heng Luo, Dian Jin +1Cross-Domain Transfer Learning
Harnessing Source Heterogeneity for Cluster-Structured Transfer Learning
Jun 3, 2026Xiaohui Yin, Jun Jin, Shane J. Sacco +2Cross-Domain Transfer Learning Transfer Learning
Department of Statistics, University of Connecticut · Department of Public Health Sciences, Henry Ford Health · Center for Population Health, University of Connecticut Health Center
AQIFormer: A Transformer-Based Multi-View Architecture for Cross-City Air Quality Classification
Jun 2, 2026Om Kathalkar, Nitin Nilesh, Sachin Chaudhari +1Multi-View Learning Air Quality Prediction
IIIT Hyderabad · India
From Local Training to Large-Scale Mapping: A Comparative Assessment of Machine Learning and Deep Learning for Transferable Satellite-Derived Bathymetry
Jun 1, 2026Hsiao-Jou Hsu, Joachim MoortgatRemote Sensing Cross-Domain Transfer Learning
School of Earth Sciences, The Ohio State University, 275 Mendenhall Laboratory, 125 South Oval Mall, Columbus, OH 43210, USA
Cross-Domain Dead Tree Detection via Knowledge Distillation in Aerial Imagery
Jun 1, 2026Anis Ur Rahman, Mete Ahishali, Einari Heinaro +1Remote Sensing Image Understanding Cross-Domain Transfer Learning
CSC – IT Center for Science Ltd., Espoo, Finland · Department of Forest Sciences, University of Helsinki · KOKO Forest Ltd. +1
Quantifying and Mitigating Domain Shift in Peach Leaf Damage Classification: Attention Mechanisms and Fine-Tuning Strategies
Jun 1, 2026Adrián Cánovas-Rodriguez, Miguel A. González-Illán, Maria Fernanda García-Cruz +5Agricultural Image Analysis Fine-Tuning
Department of Information and Communication Engineering, University of Murcia, Murcia, Spain · Department of Irrigation, Centro de Edafología y Biología Aplicada del Segura CEBAS-CSIC, Murcia, Spain
Breaking the Information Silo: Semantic Personas for Cross-Domain Recommendation
Jun 1, 2026Jonathan Mayo, Moshe Unger, Konstantin BaumanCross-Domain Transfer Learning Recommender Systems
Technology and Information Management Department, Coller School of Management, Tel Aviv University, Tel Aviv 6997801, Israel · Management Information Systems Department, Fox School of Business, Temple University, Philadelphia, Pennsylvania 19122
TERRA: Task-Embedded Reasoning and Representation Architecture for Cross-Domain Applications
Jun 1, 2026Shayan ShokriMarkov Decision Processes Markov Models
Humanpath Labs Inc.
The Right Inference Strategy Is All You Need: Nearly Training-Free Domain-Wise Inference for EgoCross Challenge
May 30, 2026Leyi Wu, Yifan Zhao, Jinjie Zhang +2Egocentric Video QA Video QA
HKUST(GZ) · Knowin · HKUST
Synthetic Data from Cross-Domain Events for Large-Scale Recommendation Systems
May 29, 2026Xiangyu Wang, Yawen He, Shivendra Pratap Singh +12Synthetic Data Augmentation Synthetic Data Generation
Meta
CoFiDA-M: Concept-Aware Feature Modulation for Cross-Domain Adaptation with Image-Only Inference
May 29, 2026Nurjahan Sultana, Moi Hoon Yap, Xinqi Fan +1Skin Lesion Classification Medical Image Classification
Department of Computing and Mathematics, Manchester Metropolitan University Dalton Building, Chester Street, M1 5GD Manchester, UK
When Do Graph Foundation Models Transfer? A Data-Centric Theory
May 28, 2026Jiajun Zhu, Ying Chen, Peihao Wang +4Graph Representation Learning Cross-Domain Transfer Learning
University of Texas at Austin · Arizona State University · Georgia Institute of Technology
Uncertainty-Aware Transfer Learning for Cross-Building Energy Forecasting: Toward Robust and Scalable District-Level Energy Management
May 28, 2026Shadmehr Zaregarizi, Khashayar YavariUncertainty Quantification Time Series Forecasting
Politecnico di Torino · Turin, Italy
When RL Suppresses Its Own Vocabulary: Recovering Reasoning Diversity in Puzzle-to-Math Transfer
May 28, 2026Mayug Maniparambil, Arjun Karuvally, Terrence Sejnowski +1RL for Language Models RL for Language Model Reasoning
Fin AI Research · Salk Institute for Biological Studies
Transfer Learning using 66 Diseases for Disease Forecasting Applications
May 26, 2026Lauren J Beesley, Alexander C Murph, Dave Osthus +1Multivariate Time Series Forecasting Epidemic Forecasting
Statistics, Los Alamos National Laboratory · Computational Intelligence & Modeling, Los Alamos National Laboratory
Does Continued Pretraining on a Learner Corpus Improve Automated Essay Scoring on English Proficiency Tests? Evidence from EFCAMDAT
May 25, 2026Duy Anh NguyenEducational Assessment Domain-Adaptive Pretraining
University of Greenwich, London, United Kingdom · FPT University, Can Tho, Vietnam
MDGMIX: Boundary-Aware Subgraph Mixing for Multi-Domain Graph Pre-Training
May 25, 2026Ziyu Zheng, Yaming Yang, Ziyu Guan +2Cross-Domain Generalization Cross-Domain Transfer Learning
School of Computer Science and Technology, Xidian University, Xi’an, China · School of Artificial Intelligence, Xidian University, Xi’an, China
Harmony in Diversity: Multi-domain Contrastive Policy Optimization for Large Reasoning Models
May 25, 2026Zongji Yu, Wenshui Luo, Yiliu Sun +5Reinforcement Learning RL for Language Model Reasoning
Shanghai Jiao Tong University · Shanghai AI Laboratory · Shandong University

Jun 14, 2026, cs.LGJ/K move · Enter open · S save

Continuous Cross-Domain Traffic State Prediction via Memory-Augmented Graph Liquid Time-Constant Networks

Jinrong Xiang, Ming Xu

Software College, Liaoning Technical University, Huludao, Liaoning 125100, China

Cross-Domain Transfer Learning Spatiotemporal Forecasting Traffic Forecasting Spatiotemporal GNNs

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Traffic state prediction is a fundamental task in intelligent transportation systems. In practical applications, some regions suffer from limited traffic observations due to insufficient sensing infrastructure, making cross-domain knowledge transfer an important solution for data-scarce traffic prediction. However, existing cross-domain traffic prediction methods still face several limitations, including coarse-grained source-target adaptation, limited capability in handling unseen target-domain patterns, and insufficient modeling of continuous traffic dynamics under irregular or heterogeneous temporal conditions. To address these issues, this paper proposes a continuous cross-domain traffic prediction framework, termed Memory-Augmented Graph Liquid Time-Constant Network (MA-GLTC). Specifically, we first construct spatio-temporal units (STUs) to decompose traffic networks into transferable local units, enabling fine-grained knowledge alignment across domains. Then, a graph liquid time-constant network (GLTC) is developed to model graph-coupled traffic evolution in continuous time. Different from generic graph neural ODE-based models, GLTC introduces graph-coupled recurrent conductance into liquid time-constant dynamics, allowing node states to evolve with leakage, adaptive time constants, and neighborhood-aware feedback. Furthermore, a Memory-based Transfer Storage (MTS) mechanism is designed to preserve source-domain knowledge, retrieve matched traffic patterns, and update reliable target-domain patterns when unseen states emerge. Experiments on five public traffic datasets demonstrate that MA-GLTC consistently outperforms representative innerdomain and cross-domain baselines in both short-term and longterm prediction tasks. Compared with the second-best method, MA-GLTC reduces the average prediction errors by 3.02%, 0.33%, 8.92%, 10.09%, and 2.11%, respectively.

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