Transfer Learning: Bridging Intelligence Across Domains

Latest 100 papers on transfer learning: Aug. 17, 2025

Transfer learning has emerged as a cornerstone of modern AI/ML, enabling models to leverage knowledge gained from one task or domain to accelerate learning and improve performance in another. This paradigm is particularly crucial in scenarios with limited data, where training models from scratch is impractical. Recent research highlights significant strides in this area, pushing the boundaries of what’s possible across diverse applications, from healthcare and robotics to material science and telecommunications.

The Big Idea(s) & Core Innovations

Many recent breakthroughs revolve around enhancing the adaptability and efficiency of models through nuanced transfer learning strategies. A key challenge addressed is the domain gap or distribution shift, where data from the source and target tasks differ significantly. For instance, in “Physics-Informed Multimodal Bearing Fault Classification under Variable Operating Conditions using Transfer Learning” by Tasfiq E. Alam et al. from the University of Oklahoma, the authors improve fault diagnosis by integrating physics-informed features and employing a Layer-Wise Adaptation Strategy (LAS) to generalize across variable operating conditions. This same principle extends to medical imaging, where “Calibrated and Robust Foundation Models for Vision-Language and Medical Image Tasks Under Distribution Shift” by Behraj Khan et al. introduces StaRFM, a framework that addresses confidence misalignment and distribution shifts in foundation models for medical image tasks and vision-language, using Fisher information penalty and confidence misalignment penalty.

Another innovative approach to bridging domain gaps comes from “Can Diffusion Models Bridge the Domain Gap in Cardiac MR Imaging?” by XC Wong et al. from the University of Leeds. They propose Diffusion-Driven Adaptation (DDA) to generate structurally consistent synthetic data, improving segmentation across different cardiac MR imaging protocols. Similarly, “Masked Autoencoder Self Pre-Training for Defect Detection in Microelectronics” by Röhrich et al. from Fraunhofer demonstrates that self-pretraining Masked Autoencoders directly on target datasets (like microelectronics images) significantly outperforms pre-training on generic natural image datasets when data is scarce. This highlights a shift towards more domain-specific pre-training for optimal transfer.

The concept of parameter-efficient fine-tuning is also seeing significant advancements. “LoRA-based methods on Unet for transfer learning in Subarachnoid Hematoma Segmentation” by Cristian Minoccheri et al. at the University of Michigan explores LoRA and its variants (CP-LoRA, DoRA) for medical image segmentation, showing improved accuracy with minimal parameter updates. This is further supported by “Regularizing Subspace Redundancy of Low-Rank Adaptation” by Yue Zhu et al. from Dalian University of Technology, who introduce ReSoRA to explicitly regularize redundancy in low-rank adaptation, leading to better feature representation and domain adaptability without inference overhead.

From a different angle, “Look the Other Way: Designing ‘Positive’ Molecules with Negative Data via Task Arithmetic” by Rıza Özçelik et al. from Eindhoven University of Technology introduces a novel ‘molecular task arithmetic’ for drug discovery, generating ‘positive’ molecules using only ‘negative’ data, eliminating the need for extensive labeled positive examples. This creative use of transfer learning highlights its potential in fields with inherently sparse positive data.

Under the Hood: Models, Datasets, & Benchmarks

The innovations above are underpinned by a rich ecosystem of models, datasets, and benchmarks:

• Models & Architectures:
  • Prompt-based Models: “SemPT: Semantic Prompt Tuning for Vision-Language Models” by Xiao Shi et al. proposes Semantic Prompt Tuning (SemPT) for vision-language models, improving cross-category generalization. “PromptTSS: A Prompting-Based Approach for Interactive Multi-Granularity Time Series Segmentation” by Ching Chang et al. from National Yang Ming Chiao Tung University uses novel prompting mechanisms for dynamic adaptation to unseen time-series patterns.
  • Vision Transformers (ViT) & EfficientNets: “Accurate Measles Rash Detection via Vision Transformer Fine-Tuning” by Qingguo Wang (Meharry Medical College) leverages DeiT for high-accuracy measles rash detection. “Improving watermelon (Citrullus lanatus) disease classification with generative artificial intelligence (GenAI)-based synthetic and real-field images via a custom EfficientNetV2-L model” by Nitin Rai et al. (University of Florida) uses a custom EfficientNetV2-L and synthetic data for agricultural disease classification. “Perch 2.0: The Bittern Lesson for Bioacoustics” by Hamer et al. (Google DeepMind) achieves state-of-the-art bioacoustic classification with EfficientNet-B3. “Transfer Learning with EfficientNet for Accurate Leukemia Cell Classification” by Faisal Ahmed (Embry-Riddle Aeronautical University) champions EfficientNet-B3 for ALL classification.
  • Deep Learning Frameworks: “UoMo: A Foundation Model for Mobile Traffic Forecasting with Diffusion Model” by Haoye Chai et al. (Tsinghua University) introduces a universal foundation model combining diffusion models and transformers for mobile traffic forecasting. “Reinforcement learning in densely recurrent biological networks” by Miles Walter Churchland and Jordi Garcia-Ojalvo introduces ENOMAD, a hybrid optimization framework for training highly recurrent biological networks, interpreting it as transfer learning. “DiFuse-Net: RGB and Dual-Pixel Depth Estimation using Window Bi-directional Parallax Attention and Cross-modal Transfer Learning” uses a novel architecture with attention and cross-modal transfer for depth estimation.
• Datasets & Benchmarks:
  • Domain-Specific Datasets: “A dataset and model for recognition of audiologically relevant environments for hearing aids: AHEAD-DS and YAMNet+” introduces AHEAD-DS, a standardized dataset for sound recognition in hearing aids. “SAR-TEXT: A Large-Scale SAR Image-Text Dataset Built with SAR-Narrator and Progressive Transfer Learning” by Xinjun Cheng et al. creates SAR-TEXT, a massive SAR image-text dataset. “CCFQA: A Benchmark for Cross-Lingual and Cross-Modal Speech and Text Factuality Evaluation” introduces CCFQA for evaluating MLLMs’ factuality across languages and modalities.
  • Medical Imaging Datasets: “Ear-Keeper: A Cross-Platform AI System for Rapid and Accurate Ear Disease Diagnosis” uses a large-scale otoendoscopy dataset. “Robust Five-Class and binary Diabetic Retinopathy Classification Using Transfer Learning and Data Augmentation” leverages the APTOS 2019 dataset for DR classification. “MRI-CORE: A Foundation Model for Magnetic Resonance Imaging” by Haoyu Dong et al. from Duke University is trained on over 6 million MRI slices, demonstrating its potential as a foundation model for medical imaging. “Improving U-Net Confidence on TEM Image Data with L2-Regularization, Transfer Learning, and Deep Fine-Tuning” offers a GitHub repository for U-Net comparisons in TEM image analysis.
• Code Repositories (selected examples for exploration):
  • PromptTSS
  • Regularized Self-Labeling
  • GDCE for X-ray harmonization
  • Physics-informed Fault Diagnosis
  • CCFQA
  • SEMNet Reconstruction
  • EfficientNet for Leukemia
  • DogFit
  • ReSoRA
  • MM-Gesture
  • Causal Invariant Causal Mechanism Estimation (CICME)
  • Diabetic Retinopathy Classification
  • DD-SAM2

Impact & The Road Ahead

The collective insights from these papers paint a vibrant picture of transfer learning’s transformative potential. We’re seeing models not just performing well on their trained tasks, but truly adapting to novel, unseen conditions with remarkable efficiency. This translates directly to real-world impact:

• Healthcare: Faster and more accurate disease diagnosis (e.g., lung cancer with “Explainable AI Technique in Lung Cancer Detection Using Convolutional Neural Networks” from Nepal, ear diseases with Ear-Keeper, diabetic retinopathy, and even pulmonary embolism from ECGs), early detection of conditions like NSCLC via cough analysis, and more robust medical image analysis despite varying equipment (X-ray harmonization, cardiac MRI).
• Industrial Automation: Improved welding defect detection in challenging maritime environments and more efficient tool wear prediction in manufacturing, critical for predictive maintenance.
• Resource Efficiency: Leveraging transfer learning to reduce the need for massive, expensively labeled datasets, as seen in self-supervised dataset distillation, microelectronics defect detection, and optical communications, makes AI more accessible and sustainable.
• Ubiquitous AI: The deployment of powerful AI on edge devices like smartphones for hearing aids (YAMNet+), heart rate monitoring (UWB Radar-based), and social interaction detection (SocialPulse) underscores a future where AI capabilities are seamlessly integrated into our daily lives.
• Scientific Discovery: From reconstructing solar EUV irradiance to predicting electron-nucleus cross sections and analyzing material behavior, transfer learning is accelerating scientific discovery in fields where data is often scarce or complex.

The road ahead involves deeper exploration into fundamental aspects of transfer learning, such as understanding and mitigating systematic biases (“Cheap Learning: Maximising Performance of Language Models for Social Data Science Using Minimal Data” and “Sensitivity of Stability: Theoretical & Empirical Analysis of Replicability for Adaptive Data Selection in Transfer Learning”). Furthermore, the development of universal foundation models like UoMo and MRI-CORE, capable of zero-shot or few-shot generalization across diverse tasks, promises to democratize AI development and accelerate innovation across countless domains. The future of AI is undeniably intertwined with the intelligent and efficient transfer of knowledge.