Transfer Learning Unleashed: Bridging Domains, Boosting Performance, and Building Smarter Systems

Latest 50 papers on transfer learning: Dec. 21, 2025

Transfer learning continues to be one of the most exciting and impactful areas in AI/ML, enabling models to leverage knowledge gained from one task or domain to accelerate learning and improve performance on another. This approach is particularly crucial when dealing with limited data, complex real-world variability, or the need for computational efficiency. Recent research showcases a burgeoning landscape of innovative applications and theoretical advancements, pushing the boundaries of what’s possible in diverse fields from healthcare to materials science and environmental monitoring.

The Big Idea(s) & Core Innovations

At its heart, transfer learning is about smart knowledge reuse. A recurring theme in recent papers is the development of frameworks that enable models to adapt to new, often challenging, conditions without costly retraining or vast amounts of new labeled data. For instance, the Pretrained Battery Transformer (PBT) by Ruifeng Tan et al. from The Hong Kong University of Science and Technology introduces the first foundation model for battery life prediction. It leverages domain-knowledge-encoded Mixture-of-Expert (MoE) layers, significantly outperforming existing models by 19.8% across diverse lithium-ion battery datasets and showing remarkable generalizability across different chemistries and operating conditions. This is a testament to how specialized architectures can embed domain knowledge to make transfer learning highly effective.

In a similar vein, “TRACER: Transfer Learning based Real-time Adaptation for Clinical Evolving Risk” by Mengying Yan et al. from Duke University tackles the critical issue of model drift in clinical settings. TRACER dynamically adapts predictive models to temporal shifts in clinical data using an Expectation-Maximization (EM) algorithm and transfer learning, mitigating the need for full model retraining during events like pandemics. This work, alongside “Diagnosis-based mortality prediction for intensive care unit patients via transfer learning” by Mengqi Xu et al. from the University of Waterloo, highlights how transfer learning can address diagnostic heterogeneity and improve mortality predictions, showcasing its immediate, life-saving impact in healthcare.

The challenge of domain mismatch and data scarcity is also addressed by “Autonomous Source Knowledge Selection in Multi-Domain Adaptation” by Keqiuyin Li et al. from the Australian Artificial Intelligence Institute. Their AutoS method autonomously selects relevant source knowledge from massive multi-domain datasets, pruning irrelevant or noisy information to enhance target task prediction. This is complemented by “Covariate-Elaborated Robust Partial Information Transfer with Conditional Spike-and-Slab Prior” (CONCERT) by Ruqian Zhang et al. from Fudan University, which uses a Bayesian approach with a conditional spike-and-slab prior to characterize partial similarities, enabling robust information transfer even when source and target domains exhibit significant discrepancies.

Beyond specialized applications, fundamental advancements in optimizing transfer learning itself are also emerging. “Optimization with Access to Auxiliary Information” by El Mahdi Chayti and Sai Praneeth Karimireddy explores how cheaper auxiliary gradients can speed up optimization, a crucial insight for settings like federated and transfer learning. Furthermore, “Robust Weight Imprinting: Insights from Neural Collapse and Proxy-Based Aggregation” by Justus Westerhoff et al. introduces the IMPRINT framework, improving weight imprinting by 4% by leveraging neural collapse and proxy-based aggregation, particularly effective in low-data regimes.

Under the Hood: Models, Datasets, & Benchmarks

Innovations in transfer learning often go hand-in-hand with new models, specialized datasets, and rigorous benchmarks:

• Pretrained Battery Transformer (PBT): A novel foundation model for battery life prediction, trained on 13 diverse lithium-ion battery (LIB) datasets from The Hong Kong University of Science and Technology and achieving superior accuracy. Code available at https://github.com/Ruifeng-Tan/PBT.
• KineMIC: Introduced in “Kinetic Mining in Context: Few-Shot Action Synthesis via Text-to-Motion Distillation” by L. Cazzola et al. from the University of Florence, this teacher-student framework adapts Text-to-Motion (T2M) models to Human Activity Recognition (HAR) tasks, achieving a +23.1% accuracy improvement in few-shot settings.
• MicroPhaseNO: From “MicroPhaseNO: Adapting an Earthquake-Trained Phase Neural Operator for Microseismic Phase Picking” by Ayrat Abdullin et al. at King Fahd University of Petroleum and Minerals, this model adapts earthquake-trained neural operators for microseismic monitoring, achieving up to 30% higher F1 score with only 200 labeled events. Code: https://github.com/ayratabd/MicroPhaseNO.
• FTBSC-KGML: Proposed in “Towards Fine-Tuning-Based Site Calibration for Knowledge-Guided Machine Learning: A Summary of Results” by Ruolei Zeng et al. from the University of Minnesota, this framework combines fine-tuning and site calibration to improve land emissions estimation by capturing spatial variability with interpretable global models.
• PULSE: A self-supervised pretraining framework for physiological time-series data, detailed in “Self-Supervised Dynamical System Representations for Physiological Time-Series” by Yenho Chen et al. from Georgia Institute of Technology, which leverages dynamical systems to preserve system information and improve transfer learning.
• BuilDa: Presented in “A Highly Configurable Framework for Large-Scale Thermal Building Data Generation to drive Machine Learning Research” by Thomas Krug et al., this framework generates synthetic thermal building data for ML research, supporting transfer learning studies by fine-tuning hundreds of models. Code: https://github.com/FZJ-IEK3-VSA/LoadProfileGenerator.
• RMAdapter: A novel approach for fine-tuning Vision-Language Models (VLMs) in few-shot scenarios, discussed in “RMAdapter: Reconstruction-based Multi-Modal Adapter for Vision-Language Models” by Xiang Lin et al. from Beihang University, using reconstruction-based learning to preserve general knowledge while adapting to specific tasks.

Impact & The Road Ahead

The impact of these advancements is profound and far-reaching. From making battery technology more reliable to enabling real-time, adaptive healthcare and more accurate environmental monitoring, transfer learning is accelerating AI’s deployment in critical applications. The theoretical insights into domain feature collapse, as presented in “Domain Feature Collapse: Implications for Out-of-Distribution Detection and Solutions” by Hong Yang et al. from Rochester Institute of Technology, are particularly vital for building robust and safe AI systems by ensuring models retain crucial domain-specific information.

Looking ahead, the emphasis will likely be on even more nuanced and efficient knowledge transfer. The development of foundation models, which can be rapidly adapted to myriad tasks with minimal data, will continue to be a significant trend. “Poodle: Seamlessly Scaling Down Large Language Models with Just-in-Time Model Replacement” by Nils Strassenburg et al. from Hasso Plattner Institute, which introduces JITR for replacing large LLMs with cheaper, specialized models, points towards a future of highly efficient and context-aware AI deployment. Moreover, the integration of transfer learning with physics-informed methods, as seen in “Probabilistic Predictions of Process-Induced Deformation in Carbon/Epoxy Composites Using a Deep Operator Network” and “Improved Physics-Driven Neural Network to Solve Inverse Scattering Problems”, promises to unlock scientific discovery and engineering innovation. The journey of transfer learning is truly dynamic, consistently reshaping how we approach complex problems and build intelligent systems for a better future.