Transfer Learning

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Period ending 2026-09-21

6 new papers

A weekly snapshot of new work published in Transfer Learning.

Period ending 2026-09-14

4 new papers

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Period ending 2026-09-07

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A weekly snapshot of new work published in Transfer Learning.

155 papers

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Reducing Annotation Burden for Femoral Cartilage Segmentation in Knee MRI via Cross-Sequence Transfer Learning
May 9, 2026Francesco Chiumento, Gianluigi Crimi, Elisa Moretta +7Knee Osteoarthritis Medical Image Segmentation
CAST: Channel-Aware Spatial Transfer Learning with Pseudo-Image Radar for Sign Language Recognition
May 9, 2026Md. Shakhoyat Rahman Shujon, Sheikh Md. Galib Mahim, Md. Milon Islam +4Sign Language Recognition Radar
Transfer Learning for Dead Fuel Moisture Prediction Using Time-Warping Recurrent Neural Networks
May 8, 2026Jonathon Hirschi, Jan Mandel, Adam KochanskiTransfer Learning Neural Network
Spectral Dynamics in Deep Networks: Feature Learning, Outlier Escape, and Learning Rate Transfer
May 8, 2026Clarissa Lauditi, Cengiz Pehlevan, Blake BordelonDeep Network Neural Network
Transfer Learning Across Fast- and Full-Simulation Domains in High-Energy Physics
May 8, 2026Matthias Schott, Lucie FlekHigh Energy Physics Transfer Learning
Closed-Form Linear-Probe Dataset Distillation for Pre-trained Vision Models
May 8, 2026Bincheng Peng, Guang Li, Ping Liu +2Chain-Of-Thought Distillation Imagenet
Knowledge Transfer Scaling Laws for 3D Medical Imaging
May 7, 2026Ho Hin Lee, Dongna Du, Chu Wang +4Transfer Learning Medical Imaging
Understanding Cross-Language Transfer Improvements in Low-Resource HTR: The Role of Sequence Modeling
May 7, 2026Sana Al-azzawi, Chang Liu, Nudrat Habib +2Cross-Lingual Transfer Handwritten Text Recognition
LANTERN: LLM-Augmented Neurosymbolic Transfer with Experience-Gated Reasoning Networks
May 6, 2026Mahyar Alinejad, Yue Wang, Amrit Singh Bedi +1Deep Reinforcement Learning Neuro-Symbolic Framework
Digital Image Forgery Detection Using Transfer Learning
May 4, 2026Fatma Betul Buyuk, Gozde Karatas Baydogmus, Ali Buldu +2Face Forgery Detection Digital Forensics
Demographic-Aware Transfer Learning for Sleep Stage Classification in Clinical Polysomnography
May 4, 2026S M Asif Hossain, Shruti KshirsagarSleep Stage Classification Sleep
Chebyshev-Augmented One-Shot Transfer Learning for PINNs on Nonlinear Differential Equations
May 2, 2026Yiqi Rao, Pavlos ProtopapasPartial Differential Equation Benchmarks Transfer Learning
The Pre-Training Study of Expanded-SPLADE Models on Web Document Titles
May 2, 2026Hiun Kim, Tae Kwan Lee, Taeryun WonLarge Language Model Pretraining Model Pretraining
Developing a Strong Pre-Trained Base Model for Plant Leaf Disease Classification
May 2, 2026David J. RichterTomato Diseases Transfer Learning
Hyperspherical Forward-Forward with Prototypical Representations
Apr 30, 2026Shalini Sarode, Brian Moser, Joachim Folz +4Single Forward Pass Backpropagation
Learning Rate Transfer in Normalized Transformers
Apr 29, 2026Boris Shigida, Boris Hanin, Andrey GromovLearning Rates Transfer Learning
Transfer Learning for Tonal Noise Prediction in VRF Units Using Thermodynamic and Vibration Signals
Apr 28, 2026ZhiWei Su, Ding Wang, Yuan Guo +2Vibration Transfer Learning
Few-Shot Cross-Device Transfer for Quantum Noise Modeling on Real Hardware
Apr 27, 2026Sahil Al Farib, Sheikh Redwanul Islam, Azizur Rahman AnikQuantum Computing Quantum Error Correction
Adapting TrOCR for Printed Tigrinya Text Recognition: Word-Aware Loss Weighting for Cross-Script Transfer Learning
Apr 22, 2026Yonatan Haile Medhanie, Yuanhua NiLatin African Languages
RADS: Reinforcement Learning-Based Sample Selection Improves Transfer Learning in Low-resource and Imbalanced Clinical Settings
Apr 22, 2026Wei Han, David Martinez, Anna Khanina +2Active Learning Transfer Learning
SMART: A Spectral Transfer Approach to Multi-Task Learning
Apr 22, 2026Boxin Zhao, Mladen Kolar, Jinchi LvTransfer Learning Multi--Task Learning
Leveraging Kernel Symmetry for Joint Compression and Error Mitigation in Edge Model Transfer
Apr 19, 2026Anis Hamadouche, Mathini SellathuraiSource-Channel Coding Transmission
Physics-Informed Neural Networks with Learnable Loss Balancing and Transfer Learning
Apr 17, 2026Reza PirayeshshirazinezhadConventional Physics-Informed Neural Networks Transfer Learning
Transfer Learning from Foundational Optimization Embeddings to Unsupervised SAT Representations
Apr 16, 2026Koyena Pal, Serdar KadiogluSatisfiability Mixed-Integer Programming
Temporal Cross-Modal Knowledge-Distillation-Based Transfer-Learning for Gas Turbine Vibration Fault Detection
Apr 16, 2026Ali Bagheri Nejad, Mahdi Aliyari-Shoorehdeli, Abolfazl HasanzadehGas Turbine Combustor Structural Health Monitoring
Transferable FB-GNN-MBE Framework for Potential Energy Surfaces: Data-Adaptive Transfer Learning in Deep Learned Many-Body Expansion Theory
Apr 10, 2026Siqi Chen, Zhiqiang Wang, Yili Shen +8Many-Body Physics Graph Neural Networks
Poodle: Seamlessly Scaling Down Large Language Models with Just-in-Time Model Replacement
Dec 5, 2025Nils Strassenburg, Boris Glavic, Tilmann RablLarge Language Model Baselines Transfer Learning
Label-Efficient 3D Forest Mapping: Self-Supervised and Transfer Learning for Instance Segmentation, Semantic Segmentation, and Species Classification
Nov 9, 2025Aldino Rizaldy, Fabian Ewald Fassnacht, Ahmed Jamal Afifi +33D Semantic Segmentation Boreal Forests
Divergence-Based Similarity Function for Multi-View Contrastive Learning
Jul 9, 2025Jaehyoung Jeon, Cheolsu Lim, Myungjoo KangContrastive Learning Multi-View
GenTL: A General Transfer Learning Model for Building Thermal Dynamics
Jan 23, 2025Fabian Raisch, Thomas Krug, Christoph Goebel +1Transfer Learning Bidirectional Long Short-Term Memory
Fixed-Gaussian Spectral Algorithms: Minimax Optimal Rates for Misspecified Learning and Transfer
Jan 18, 2025Haotian Lin, Matthew ReimherrKernel Ridge Regression Transfer Learning
Generalization error of min-norm interpolators in transfer learning
Jun 20, 2024Yanke Song, Kenneth Gu, Sohom Bhattacharya +1Interpolation Empirical Risk Minimization
DIRA-SS:Dynamic Domain Incremental Regularised Adaptation -- Self-Supervised
Nov 13, 2023Abanoub Ghobrial, Kerstin EderDomain Adaptation Self-Supervised
Limits of Transfer Learning
Jun 23, 2020Jake Williams, Abel Tadesse, Tyler Sam +2Transfer Learning Machine Learning
Guided Adversarial Robust Transfer Learning with Source Mixing
Date pendingXin Xiong, Zijian Guo, Tianxi CaiTransfer Learning Adversarial Transferability

May 9, 2026 · cs.CVJ/K move · Enter open · S save

Reducing Annotation Burden for Femoral Cartilage Segmentation in Knee MRI via Cross-Sequence Transfer Learning

Francesco Chiumento, Gianluigi Crimi, Elisa Moretta, Rocco Milieri+6

Purpose: To develop and evaluate cross-sequence transfer learning for automatic femoral cartilage segmentation, testing bidirectional transfer between dual-echo steady-state (DESS) and sagittal proton density-weighted 3D fast spin-echo (Cube) sequences. Materials and Methods: We optimized a modified 2D U-Net on 507 DESS images from the Osteoarthritis Initiative (OAI). We then established same-sequence baselines using subject-level cross-validation on a subset of 44 OAI DESS images and 44 Cube images acquired at the Istituto Ortopedico Rizzoli, Bologna, Italy. Each subset included 22 non-lesioned and 22 lesioned subjects. Finally, we performed transfer learning across sequences by fine-tuning the pretrained models on the target sequence with increasing training set sizes to study convergence, while keeping validation and test sets fixed. Segmentations were evaluated using Dice similarity coefficient (DSC) and average surface distance (ASD). Lesion effects were assessed with two-sided Mann-Whitney U tests with Bonferroni correction. Results: Same-sequence training yielded higher accuracy on DESS than Cube (DSC,

0.900

0.830

;

P < .001

). Cube-to-DESS transfer matched DESS performance (DSC,

0.903 \pm 0.032

0.900 \pm 0.027

), reaching a performance plateau at 9 training subjects. DESS-to-Cube yielded a lower combined DSC (

0.802 \pm 0.049

0.830 \pm 0.042

), reaching a plateau at 24 training subjects. Lesions did not affect DESS (

P \ge .39

) but reduced Cube accuracy (DSC,

0.805

0.856

;

P < .001

). Conclusion: Transfer learning across sequences can substantially reduce target-sequence annotation requirements for femoral cartilage segmentation, but performance is direction- and sequence-dependent, and the effects of lesions on segmentation may vary across MRI sequences.

Knee Osteoarthritis Medical Image Segmentation Magnetic Resonance Imaging Transfer Learning Cubifygs Annotations

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