Transfer Learning

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Overview

Transfer learning asks how a model trained on one task or domain can help with another, reducing the need for target-specific data and computation. Common approaches include pretraining followed by fine-tuning or parameter-efficient adapters, feature alignment for domain shifts, and knowledge transfer through distillation, model merging, or shared representations. Evaluations span zero-shot and few-shot transfer, cross-domain benchmarks in vision and language, and adaptation in settings such as clinical data, robotics, and scientific modeling. A central tension is that transferred knowledge can improve accuracy and sample efficiency or cause negative transfer; predicting when it will help, and separating reusable knowledge from dataset or architecture effects, remains difficult.

Latest papers

CardsList

422 papers62 papers in the last four weeks, up 29% on the four weeks before. 0.4% of all new papers.

  1. Cross-Material Support Transfer for Core-Loss Prediction Under Waveform Covariate Shift

    Sep 15, 2026Cong Yao, Chunye GongMaterials Property PredictionTransfer Learning

    School of Computer Science and Technology, Changsha University of Science and Technology, Changsha 410076, China · College of Computing, National University of Defense Technology, Changsha 410073, China · National Supercomputer Center in Tianjin, Tianjin 300457, China +1

  2. Can Knowledge Transfer Parameters Be Learned? LePoKet for Efficient Robotic Vision

    Sep 15, 2026Yanick C. Tchenko, Felix Mohr, Hicham Hadj-Abdelkader +1Cross-Architecture Knowledge DistillationTransfer Learning

    Universit´e Paris-Saclay, IBISC Laboratory, France. · Universidad de La Sabana, Colombia.

  3. Transfer Learning for Socioeconomic Estimation in Forced-Displacement Settings

    Sep 14, 2026Steven Ndung'u, Adel Daoud, Ismael Yacoubou Djima +2Vision TransformerRemote Sensing

    The United Nations High Commissioner for Refugees, Denmark · Chalmers University, Sweden · The World Bank, USA

  4. Single-condition neural solvers encode transferable response spaces for parametric differential equations

    Sep 14, 2026Wenbo Cao, Weiwei ZhangPDE SolvingPDE Surrogate Modeling

    Institute of AI for Industries, Chinese Academy of Sciences, Nanjing 211135, China · Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China · School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China +2

  5. Multi-source Transfer Learning of Time Series with a Shapelet-based Distance Measure

    Sep 14, 2026Jiseok Lee, Brian Kenji IwanaTime Series ClassificationTransfer Learning

    Graduate School of Information Science and Electrical Engineering, Kyushu University, 744 Motooka, Nishi Ward, Fukuoka, 819-0395, Fukuoka, Japan · Faculty of Information Science and Electrical Engineering, Kyushu University, 744 Motooka, Nishi Ward, Fukuoka, 819-0395, Fukuoka, Japan

  6. World Models for Cross-Machine CNC Transfer under Partial Sensor Overlap

    Sep 13, 2026Ayoub Louaye Bouaziz, Matthieu Ostertag, Anton DemaslesManufacturingWorld Model Learning

    Université de Bretagne Occidentale, Brest, France · Mines Nancy, Université de Lorraine, Nancy, France

  7. Towards Identifying the Dataset Biases Causing Phantom Transfer

    Sep 13, 2026Jonas Jürß, Pietro LiòDataset BiasSocial Bias in Language Models

    University of Cambridge Cambridge, CB3 0FD, UK

  8. Transfer Learning for Evolving Domains

    Sep 11, 2026Ricardo Ribeiro Pereira, Jacopo Bono, Hugo Ferreira +4Cross-Domain Transfer LearningTransfer Learning

    Feedzai · University of Porto

  9. Very Exciting: Zero-Shot Model Predictive Control of Buildings via Excitation-Based Generalized Transfer Learning Models

    Sep 11, 2026Fabian Raisch, Felix Koch, Zack Xuereb Conti +2Building Energy ManagementModel Predictive Control

    Technical University of Applied Sciences Rosenheim, Hochschulstraße 1, Rosenheim, 83024, Germany · Technical University of Munich, Arcisstraße 21, Munich, 80333, Germany · Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany +1

  10. LifeMem: Enabling Lifelong Experience Reuse for LLM Agents

    Sep 11, 2026Yuli Qiu, Yutong Li, Wei Su +5Continual Learning for LLM AgentsLLM Agent Skill Learning

    School of Computer Science and Technology, Beijing Institute of Technology · School of Computer Science and Engineering, Beihang University · Research Center for Social Computing and Interactive Robotics, Harbin Institute of Technology +1

  11. Distance generalization in transformers: why bother with positional encoding?

    Sep 10, 2026Daniel Henrik Nevermann, Claudius GrosOOD GeneralizationPositional Encoding

    Institute for Theoretical Physics, Goethe University Frankfurt, Germany

  12. BrainTaskonomy: Learning How to Pretrain and What to Transfer in fMRI Foundation Models

    Sep 9, 2026Junfeng Xia, Wenhao Ye, Junxiang Zhang +3Medical Imaging Foundation ModelsfMRI Foundation Models

    Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China · Department of Computer Science, University of Warwick, The UK · Omni-Intelligence, Shenzhen, China +1

  13. Learning with Covariance Matrices: Principal Component Analysis Meets Learning with Graphs

    Sep 9, 2026Saurabh Sihag, Andrea Cavallo, Elvin Isufi +2Graph Neural NetworksBrain Age Prediction

    Department of Electrical and Computer Engineering at the University at Albany, SUNY, Albany, NY · Delft University of Technology, Delft, The Netherlands · Department of Electrical and Computer Engineering at the University of Rochester, Rochester, NY +1

  14. Geometry Without Coordinates: LiDAR Diffusion as a 3D Feature Bridge

    Sep 9, 2026Samed Doğan, Nico Leuze, Alfred Schöttl3D Semantic SegmentationTransfer Learning

    Department of Electrical Engineering and Information Technology Munich University of Applied Sciences Munich, Bavaria 80335

  15. Chimaera: A Mixture-of-Graph-Experts Architecture for Cross-Task and Cross-Dataset Graph Learning

    Sep 8, 2026Jonatan Frank, David Richerby, Ansgar ScherpTransfer LearningText-Attributed Graph Learning

    University of Ulm, Ulm, Germany · University of Essex, Colchester, UK

  16. Cross-modal learning for SAR target recognition using optical vision foundation models

    Sep 7, 2026Lucas Hirsch, James R. Hopgood, Javid Khan +2Cross-Modal AlignmentCross-Modal Representation Transfer

    School of Engineering, University of Edinburgh, United Kingdom · Leonardo UK, United Kingdom · School of Engineering and Physical Sciences, Heriot-Watt University, United Kingdom

  17. IIns-VAE+: A Robust Transfer Learning Framework for Environmental Identification in Wireless Sensing

    Sep 5, 2026Yuxiao Li, Keke Hu, Bobai Zhao +2Transfer LearningClassification

    Basque Center for Applied Mathematics (BCAM), 48009 Bilbao, Spain · College of Semiconductor (College of Integrated Circuits), Hunan University, Changsha 410082, China · College of Computer Science, Beijing Information Science and Technology University, Beijing 100192, China +2

  18. A Generalizable Feature Extractor for Alzheimer's-Related Brain MRI Tasks

    Sep 4, 2026Reza Rajabli, D. Louis CollinsMedical Imaging Foundation ModelsTransfer Learning

    McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University · Department of Biomedical Engineering, McGill University

  19. Wireless Foundation Models: State-of-the-Art and Open Challenges

    Sep 4, 2026Alonso M. Pacheco Huachaca, Juan J. Rodriguez Rodriguez, Ahmed Aboulfotouh +3Wireless Foundation ModelsWireless Communications

    Institute of Computing, University of Campinas, SP, Brazil · Department of Electrical and Software Engineering, University of Calgary, Calgary, AB, Canada

  20. Learnable composition for neural operators

    Sep 2, 2026Zituo Chen, Baiming Zhang, Sili DengNeural Surrogate ModelingPorous Media Flow

    MIT MechE · University of Toronto

  21. PlantC2USeg: Cross-Scale Consistent Pre-Training for Few-Shot Unified Plant Point Cloud Segmentation

    Sep 2, 2026Yu Tian, Xintong Jiang, Jan Franklin Adamowski +23D Point Cloud SegmentationTransfer Learning

    McGill University, 21111 Lakeshore Road, Sainte-Anne-de-Bellevue, H9X 3V9, Quebec, Canada

  22. Do Depressive Facial Patterns Transfer Across Cultures and Contexts? Evidence from a German RCT and E-DAIC

    Sep 2, 2026Misha Sadeghi, Robert Richer, Lydia Helene Rupp +7Depression DetectionCross-Dataset Generalization

    Dept. AIBE, FAU Erlangen-Nürnberg Munich Center for Machine Learning Germany · Dept. of Clinical Psychology and Psychotherapy, FAU Erlangen-Nürnberg Germany · Dept. of Computer Science, FAU Erlangen-Nürnberg Germany +1

  23. SkillGLoW: Procedural-Family Skill Consolidation for Self-Improving Agents on Long-Horizon Task Streams

    Sep 2, 2026Ao Yan, Xin Zhang, Jiawei Du +1Memory Management for LLM AgentsLLM Agent Skill Learning

    National University of Singapore, Singapore · Institute of Advanced Intelligence and Computing (IAIC), Singapore

  24. Prototype-guided transfer of sparse literature knowledge for electrolyte additive discovery

    Sep 2, 2026Weixiang Hong, Hongting Du, Jiayue Tang +4Lithium-Ion BatteriesTransfer Learning

    Sustainable Energy and Environment Thrust and Guangzhou Municipal Key Laboratory of Materials Informatics, The Hong Kong University of Science and Technology (Guangzhou), Nansha, Guangzhou, 511400, Guangdong, P.R. China · Data Science and Analytics Thrust and Guangzhou Municipal Key Laboratory of Materials Informatics, The Hong Kong University of Science and Technology (Guangzhou), Nansha, Guangzhou, 511400, Guangdong, P.R. China · Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, P.R. China +1

  25. SMart: A Multi-source Multi-phase Time Series Representation Transfer Framework

    Sep 2, 2026Fang He, Wang-chien LeeTime Series Representation LearningTransfer Learning

    Department of Computer Science and Engineering, The Pennsylvania State University, PA, USA

  26. Prior-Guided Implicit Neural Representations for Single-Subject Diffusion MRI Super-Resolution

    Sep 1, 2026Abdulkader Ghandoura, Marsil Zakour, William Consagra +1Implicit Neural RepresentationsMedical Image Super-Resolution

    Psychiatry Neuroimaging Laboratory Brigham and Women’s Hospital Harvard Medical School Boston, MA, USA · School of Computation, Information and Technology Technical University of Munich Munich, Germany · Department of Statistics University of South Carolina Columbia, SC, USA

  27. Let Prompts Bridge Defense Knowledge: Transferable Graph Purification via Vulnerability-Aware GPL

    Aug 29, 2026Shuomin Xue, Jingyuan Li, Ju Jia +2Graph Prompt LearningGraph Transfer Learning

    Southeast University Nanjing, China · Nanyang Technological University Singapore, Singapore

  28. Learning to Transfer Across Modes: Towards Unified Urban Mobility Forecasting

    Aug 28, 2026Yixuan Zhao, Man LuoRepresentation LearningTransfer Learning

    University of Exeter Department of Computer Science Exeter, United Kingdom

  29. Generalized Context in Cross Attention for Transfer Learning of Disjoint Tabular Data

    Aug 28, 2026Kazi F. Akhter, Ibna Kowsar, Manar D. SamadCross-Domain Transfer LearningTransfer Learning

    Department of Computer Science Tennessee State University Nashville, TN, USA · Department of Computer Science North Carolina Agricultural and Technical State University Greensboro, NC, USA

  30. Toward Equitable Low-Carbon Mobility: Fairness-Aware Demand Prediction for Expanding Bike-Sharing Systems

    Aug 26, 2026Man Luo, Yixuan ZhaoGraph Neural NetworksGroup Fairness

    Department of Computer Science, University of Exeter, Exeter EX4 4QF, U.K.