Transfer Learning Unleashed: Bridging Domains, Enhancing Trust, and Driving Innovation

Latest 50 papers on transfer learning: Nov. 30, 2025

Transfer learning continues to be a cornerstone of modern AI/ML, enabling models to leverage knowledge from one domain to excel in another, especially in data-scarce scenarios. Recent research showcases incredible advancements, from revolutionizing medical diagnostics and cybersecurity to enabling energy-efficient manufacturing and intuitive robotics. Let’s dive into the latest breakthroughs that are pushing the boundaries of what’s possible.### The Big Idea(s) & Core Innovationscore challenge many of these papers address is how to effectively transfer knowledge while maintaining accuracy, efficiency, and trustworthiness across diverse tasks and data distributions. We see a strong emphasis on geometric alignment in computer vision, task-centric adaptation in database systems, synthetic data generation for scarce domains, and interpretable knowledge sharing between models.studies focus on enhancing domain adaptation through sophisticated geometric understanding. For instance, the paper Disentangled Geometric Alignment with Adaptive Contrastive Perturbation for Reliable Domain Transfer by Emma Collins et al. from University of Cambridge, Stanford University, MIT, Google Research, and DeepMind introduces GAMA++, a framework that disentangles task-relevant features from nuisance factors, achieving state-of-the-art semantic alignment. Complementing this, Hana Satou et al., in Geometrically Regularized Transfer Learning with On-Manifold and Off-Manifold Perturbation, present MAADA, which unifies adversarial training and data augmentation by decomposing perturbations into on-manifold (semantic) and off-manifold (robustness) components. This distinction is crucial for improving cross-domain generalization and structural robustness.the realm of model management, Sai Wu et al. from Zhejiang University, China introduce MorphingDB: A Task-Centric AI-Native DBMS for Model Management and Inference. This groundbreaking system integrates deep learning directly into PostgreSQL, allowing users to define tasks instead of manually managing models. It leverages a transfer learning framework for fast and accurate model selection based on task characteristics, significantly improving inference efficiency with novel Mvec tensor representation and DAG-based batch pipelines.data scarcity and imbalance, especially in critical domains like medical AI, is another significant theme. Abolfazl Moslemi and Hossein Peyvandi from Sharif University of Technology, Tehran, Iran, in their work Pretraining Transformer-Based Models on Diffusion-Generated Synthetic Graphs for Alzheimer’s Disease Prediction, propose a framework using diffusion-based synthetic data generation for pretraining Graph Transformer encoders. This mitigates label imbalance and data scarcity, leading to improved generalization in Alzheimer’s diagnosis. Similarly, Dylan Saeed et al. from the University of New South Wales explore Machine-Learning Based Detection of Coronary Artery Calcification Using Synthetic Chest X-Rays, demonstrating that digitally reconstructed radiographs (DRRs) from CT scans can serve as a scalable training domain for detecting coronary artery calcification, even outperforming large pre-trained networks with lightweight CNNs.model performance, the necessity for trustworthy and interpretable AI is paramount. A comprehensive survey, Trustworthy Transfer Learning: A Survey by Jun Wu and Jingrui He, highlights the critical balance between transfer performance and trustworthiness, emphasizing privacy, fairness, robustness, and transparency. John Doe and Jane Smith introduce Model-to-Model Knowledge Transmission (M2KT): A Data-Free Framework for Cross-Model Understanding Transfer, enabling knowledge sharing between models without labeled data, crucial for interpretability and collaboration between diverse AI systems. For instance, ExplainRec: Towards Explainable Multi-Modal Zero-Shot Recommendation with Preference Attribution and Large Language Models from Author A and Author B leverages large language models and preference attribution for transparent, zero-shot recommendations.### Under the Hood: Models, Datasets, & Benchmarksresearch is bolstered by the introduction of new architectural designs, datasets, and strategic benchmarking. Here’s a glimpse into the key resources enabling these innovations:MorphingDB: A task-centric AI-native DBMS with specialized schemas and Mvec tensor data types for efficient storage and inference of neural networks within PostgreSQL. Public code: https://github.com/MorphingDB/MorphingDBMetaRank: A meta-learning framework for task-aware Model Transferability Estimation (MTE) that leverages semantic embeddings of datasets and metrics to rank optimal metrics.Logos Dataset: The largest Russian Sign Language (RSL) dataset, featuring diverse signers and vocabulary, crucial for cross-language transfer learning in sign language recognition. URL: https://arxiv.org/pdf/2505.10481MultiBanAbs: A comprehensive multi-domain Bangla abstractive text summarization dataset, comprising 54,620 articles and summaries, fostering NLP research in low-resource languages. Resources include https://www.kaggle.com/datasets/naeem711chowdhury/multibanabs.MATRIX Dataset: A multi-drone, multi-view dataset of synchronized drone footage with extensive annotations for pedestrian detection and tracking in complex urban environments. Code: https://github.com/KostaDakic/MATRIX/tree/mainMedImageInsight: A foundational medical imaging model for thoracic cavity health classification from Chest X-rays, demonstrating strong performance after fine-tuning. Code: https://github.com/microsoft/healthcareai-examplesFlood-LDM: The first diffusion-based framework for high-resolution flood-map super-resolution, leveraging physics-informed inputs for interpretability and real-time forecasting. Code: https://github.com/neosunhan/flood-diffStro-VIGRU: A baseline model for brain stroke classification that combines pre-trained Vision Transformers with Bi-GRU layers, using data augmentation to address class imbalance.LandSegmenter: A flexible foundation model for land use and land cover mapping, trained with weak supervision and a confidence-guided fusion strategy. Code: https://github.com/zhu-xlab/LandSegmenter.gitdp-VAE: A variational inference-based representation learning framework for spatial reconstruction from gene expression data, validated across 27 public datasets.SmallML: A Bayesian transfer learning framework integrating SHAP-based prior extraction, hierarchical Bayesian pooling, and conformal prediction for small-data predictive analytics in SMEs.PARS Pretraining: A self-supervised learning method for EEG signal analysis that predicts relative temporal shifts between window pairs, outperforming existing SSL methods in label-efficient and transfer learning settings.RIF Framework: For 3D anomaly detection, it uses Point Coordinate Mapping (PCM) to create rotationally invariant representations for robust feature extraction in point clouds. Code: https://github.com/hzzzzzhappy/RIF### Impact & The Road Aheadinnovations highlighted in these papers signify a profound shift in how we approach AI/ML development. Transfer learning is no longer just about using a pre-trained model; it’s about intelligent, context-aware, and trustworthy knowledge transfer. The ability to perform complex tasks with limited data, adapt to dynamic environments, and provide transparent explanations will accelerate AI adoption in critical sectors.enabling assistive mobile robots to react more intuitively to human intentions through EEG signals, as shown by Xiaoshan Zhou et al. from the University of Michigan in Feasibility of Embodied Dynamics Based Bayesian Learning for Continuous Pursuit Motion Control of Assistive Mobile Robots in the Built Environment, to optimizing energy consumption in manufacturing with deep learning, as demonstrated by Mohamed Abdallah and Mohamed Hamed Salem in Artificial intelligence approaches for energy-efficient laser cutting machines, the practical implications are vast. The insights from Source-Optimal Training is Transfer-Suboptimal by Li, Y. et al. challenge us to rethink pretraining strategies, suggesting that optimizing a source model purely for its own task might be detrimental to its transferability, opening avenues for “transfer-optimal” regularization.ahead, the focus will intensify on developing more robust, generalizable, and privacy-preserving transfer learning techniques. The integration of causal inference, as explored in Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning by Yihong Gu et al., will further enhance our ability to understand and predict outcomes in complex, heterogeneous environments. The development of foundational models like LandSegmenter and MedImageInsight, capable of zero-shot inference and fine-tuning on diverse tasks, promises to democratize AI, making sophisticated tools accessible even in resource-constrained settings. The future of transfer learning is bright, promising more efficient, intelligent, and human-centric AI systems across all domains.