Transfer Learning Unleashed: Bridging Domains, Boosting Performance, and Enhancing Interpretability

Latest 50 papers on transfer learning: Dec. 13, 2025

Transfer learning continues to be a cornerstone of modern AI/ML, empowering models to generalize across tasks and domains, especially where data is scarce. Recent research showcases remarkable strides, extending transfer learning’s reach from the intricacies of language models and medical diagnostics to the complexities of materials science and robotic manipulation. This digest dives into a collection of cutting-edge papers that are not just refining existing techniques but also pioneering entirely new paradigms for knowledge transfer.

The Big Idea(s) & Core Innovations

The central theme across these papers is the drive to make AI models more adaptable, efficient, and interpretable by strategically leveraging existing knowledge. One significant area of innovation lies in reducing computational burden and data dependency. For instance, Guided Transfer Learning for Discrete Diffusion Models by Julian Kleutgens, Claudio Battiloro, and colleagues from Harvard University and ETH Zürich introduces GTL, a framework that allows discrete diffusion models to adapt to new domains without fine-tuning the denoiser. This dramatically slashes training costs and makes scalable language modeling feasible for large vocabularies. Similarly, Poodle, featured in Poodle: Seamlessly Scaling Down Large Language Models with Just-in-Time Model Replacement from Hasso Plattner Institute, pioneers Just-in-Time Model Replacement (JITR). This system intelligently swaps large language models (LLMs) with cheaper, specialized surrogate models for recurring tasks, yielding substantial cost and energy savings without sacrificing performance.

Another crucial innovation addresses heterogeneity and feature mismatch across domains. R^2-HGP: A Double-Regularized Gaussian Process for Heterogeneous Transfer Learning by Author A, Author B, and Author C from institutions like the Institute of Advanced Computing, University X, introduces double regularization for Gaussian Processes, improving generalization and adaptability in diverse data environments. Building on this, Heterogeneous transfer learning for high-dimensional regression with feature mismatch by Jae Ho Chang, Massimiliano Russo, and Subhadeep Paul from The Ohio State University presents a statistical framework that imputes missing features in target domains using rich source data, addressing the challenging problem of feature mismatch in high-dimensional regression. In a similar vein, Covariate-Elaborated Robust Partial Information Transfer with Conditional Spike-and-Slab Prior by Ruqian Zhang and co-authors introduces CONCERT, a Bayesian method for partial information transfer, using a conditional spike-and-slab prior to model covariate-specific similarities, robustly handling discrepancies between source and target data.

The papers also highlight advancements in integrating physics and domain knowledge with deep learning. The Improved Physics-Driven Neural Network to Solve Inverse Scattering Problems by Yutong Du and an international team proposes an improved physics-driven neural network (IPDNN) framework, using a novel GLOW activation function and dynamic subregion identification alongside transfer learning to enhance electromagnetic inverse scattering solutions. For structural health monitoring, Crack detection by holomorphic neural networks and transfer-learning-enhanced genetic optimization from Aarhus University and SIGMA Clermont combines holomorphic neural networks (HNNs) with transfer learning-enhanced genetic algorithms for significantly faster and accurate crack detection in 2D solids.

Finally, several works are pushing the boundaries of interpretability and novel data modalities. Revealing economic facts: LLMs know more than they say by Marcus Buckmann, Quynh Anh Nguyen, and Ed Hill from the Bank of England demonstrates that LLMs’ hidden states encode richer economic information than their text outputs, enabling more accurate data imputation. In the realm of medical AI, Deep learning for autism detection using clinical notes: A comparison of transfer learning for a transparent and black-box approach by Gondy Leroy et al. from the University of Arizona underscores that transparent BioBERT-based models, enhanced with mixed-data training, outperform black-box approaches for ASD diagnosis from clinical notes.

Under the Hood: Models, Datasets, & Benchmarks

This wave of research leverages and introduces a diverse array of models, datasets, and benchmarks, showcasing the versatility and growing sophistication of transfer learning approaches:

• Language Models & NLP:
  • GTL (Guided Transfer Learning for Discrete Diffusion Models) employs a compact ratio network with discrete diffusion models. Public code is available: https://github.com/.
  • Poodle (Poodle: Seamlessly Scaling Down Large Language Models with Just-in-Time Model Replacement) utilizes an efficient model search framework to replace LLMs with specialized surrogate models like BERT. Code: https://github.com/hpi-potsdam/poodle.
  • PDFTEMRA (A Patient-Doctor-NLP-System to Contest Inequality for Less Privileged) is a compact transformer-based network using model distillation (GPT2 as distributor), Hartley Transform, and AdaNorm on the custom PADT medical dataset. Code: https://drive.google.com/drive/folders/1aUeGJ29DvQ98RapGGoSrQCabaYFbMxuP?usp=drive_link.
  • BanglaSentNet (BanglaSentNet: An Explainable Hybrid Deep Learning Framework for Multi-Aspect Sentiment Analysis with Cross-Domain Transfer Learning) integrates LSTM, BiLSTM, GRU, and BanglaBERT, trained on a large-scale, 8,755-review Bangla e-commerce dataset.
  • TSRCDF-SS (A transfer learning approach for automatic conflicts detection in software requirement sentence pairs based on dual encoders) uses dual encoders (SBERT, SimCSE) with a hybrid loss FFNN classifier. Code: https://github.com/yizhengwang/tsrcdf-ss.
  • MultiBanAbs (MultiBanAbs: A Comprehensive Multi-Domain Bangla Abstractive Text Summarization Dataset) introduces a large-scale (54,620 articles) multi-domain Bangla summarization dataset.
• Computer Vision & 3D Data:
  • RMAdapter (RMAdapter: Reconstruction-based Multi-Modal Adapter for Vision-Language Models) fine-tunes Vision-Language Models (VLMs) in few-shot scenarios using a dual-branch architecture.
  • SOP^2 (SOP^2: Transfer Learning with Scene-Oriented Prompt Pool on 3D Object Detection) applies prompt tuning on large-scale datasets like Waymo for 3D object detection.
  • HPNet, IAE, and Masked Autoencoders (Representation Learning for Point Cloud Understanding) are new models for point cloud primitive segmentation, with code available at https://github.com/simingyan/HPNet, https://github.com/simingyan/ImplicitAutoEncoder, and https://github.com/simingyan/MaskedAutoencoder.
  • GAMA++ (Disentangled Geometric Alignment with Adaptive Contrastive Perturbation for Reliable Domain Transfer) and MAADA (Geometrically Regularized Transfer Learning with On-Manifold and Off-Manifold Perturbation) are frameworks for domain adaptation using latent space disentanglement and geometry-aware perturbations.
  • ForamDeepSlice (ForamDeepSlice: A High-Accuracy Deep Learning Framework for Foraminifera Species Classification from 2D Micro-CT Slices) is an ensemble model of ConvNeXt-Large and EfficientNetV2-Small, trained on a rigorous dataset of 109,617 2D micro-CT slices for foraminifera classification. Code: https://github.com/KAUST-VisualizationCoreLab/ForamDeepSlice.
• Materials Science & Engineering:
  • R^2-HGP (R^2-HGP: A Double-Regularized Gaussian Process for Heterogeneous Transfer Learning) is a double-regularized Gaussian Process, code: https://github.com/r2hgp/R2HGP.
  • DTW-TL framework (An Additive Manufacturing Part Qualification Framework: Transferring Knowledge of Stress-strain Behaviors from Additively Manufactured Polymers to Metals) uses Dynamic Time Warping (DTW) and transfer learning on a dataset of four polymers and three metals.
  • Open Polymer Challenge (Open Polymer Challenge: Post-Competition Report) introduces a large-scale, open-source polymer dataset (10K+ polymers) and the ADEPT tool. Code: https://github.com/sobinalosious/ADEPT.
• Medical & Health AI:
  • CITL (A Lightweight Transfer Learning-Based State-of-Health Monitoring with Application to Lithium-ion Batteries in Unmanned Air Vehicles) is a lightweight transfer learning framework for SOH monitoring of Li-ion batteries.
  • Diagnosis-based mortality prediction (Diagnosis-based mortality prediction for intensive care unit patients via transfer learning) employs GLM and XGBoost models trained on the eICU Collaborative Research Database.
  • Tensor Kernel Machines (Adapting Tensor Kernel Machines to Enable Efficient Transfer Learning for Seizure Detection) is adapted for EEG-based seizure detection.
  • PULSE (Self-Supervised Dynamical System Representations for Physiological Time-Series) is a cross-reconstruction-based pretraining objective for physiological time series.
  • Alzheimer’s Disease Prediction (Pretraining Transformer-Based Models on Diffusion-Generated Synthetic Graphs for Alzheimer’s Disease Prediction) combines class-conditional DDPMs with Graph Transformer encoders for synthetic data pretraining.
• Miscellaneous:
  • SpecMatch-CL (Graph Contrastive Learning via Spectral Graph Alignment) is a spectral regularizer for graph contrastive learning. Code: github.com/manhbeo/GNN-CL.
  • BuilDa (A Highly Configurable Framework for Large-Scale Thermal Building Data Generation to drive Machine Learning Research) is a framework for generating synthetic thermal building data. Code: https://github.com/FZJ-IEK3-VSA/LoadProfileGenerator.
  • From One Attack Domain to Another (From One Attack Domain to Another: Contrastive Transfer Learning with Siamese Networks for APT Detection) uses Siamese contrastive transfer, XAI-guided feature selection, and an attention-based autoencoder backbone with DARPA traces.
  • Binary-30K (Binary-30K: A Heterogeneous Dataset for Deep Learning in Binary Analysis and Malware Detection) is a large-scale dataset for binary analysis and malware detection, with code at https://huggingface.co/mjbommar/binary-30k.
  • MorphingDB (MorphingDB: A Task-Centric AI-Native DBMS for Model Management and Inference) is an AI-native DBMS that integrates ML capabilities into PostgreSQL. Code: https://github.com/MorphingDB/MorphingDB.

Impact & The Road Ahead

These advancements herald a future where AI systems are not only more powerful but also more accessible and responsible. The ability to transfer knowledge across diverse domains, whether from polymers to metals or terrestrial sounds to underwater acoustics, significantly reduces the need for vast, expensive, and often scarce labeled datasets. This democratizes AI development, opening doors for smaller organizations and addressing critical challenges in resource-constrained environments, such as medical NLP for underserved communities or localized climate forecasting in the Global South.

The emphasis on lightweight models and efficient adaptation methods (like GTL and JITR) is crucial for deploying AI on edge devices and in real-time applications, from UAV battery monitoring to structural health inspection. Furthermore, the push for interpretability, seen in works like the transparent ASD detection model or BanglaSentNet’s explainable sentiment analysis, fosters trust and enables better human-AI collaboration.

Looking ahead, the research points towards increasingly sophisticated frameworks that can handle greater data heterogeneity, leverage implicit knowledge within models more effectively, and provide stronger theoretical guarantees for transfer performance. The continued convergence of deep learning with domain-specific knowledge—be it physics, biology, or economic principles—promises AI solutions that are not just intelligent but also deeply insightful and practically robust. The journey of transfer learning is far from over, and these papers illuminate exciting pathways toward a more adaptive, efficient, and equitable AI landscape.