Transfer Learning: Bridging Gaps and Boosting Performance Across AI’s Frontier

Latest 50 papers on transfer learning: Sep. 29, 2025

From predicting battery temperatures to enhancing low-resource language translation, transfer learning continues to be a pivotal force driving innovation across the AI/ML landscape. This powerful paradigm allows models to leverage knowledge gained from one task or domain to accelerate learning and improve performance on another, often overcoming challenges like data scarcity and computational overhead. Recent research highlights exciting breakthroughs, demonstrating how transfer learning is making AI systems more efficient, robust, and accessible.

The Big Idea(s) & Core Innovations

Many recent advances in transfer learning focus on adaptability and efficiency, enabling models to perform complex tasks with less data and fewer parameters. For instance, in computer vision, the paper “Adversarial Robustness of Discriminative Self-Supervised Learning in Vision” by Ömer Veysel Çağatan et al. at Koç University shows that discriminative self-supervised learning (SSL) models often outperform supervised ones in adversarial robustness for classification and transfer learning, though this advantage lessens in segmentation and detection. Similarly, “Cross-Domain Underwater Image Enhancement Guided by No-Reference Image Quality Assessment: A Transfer Learning Approach” introduces Trans-UIE, a method from Tsinghua University that uses transfer learning with no-reference image quality assessment (NR-IQA) to significantly reduce the domain gap between underwater and above-water images, improving enhancement for real-world scenarios.

In natural language processing (NLP), transfer learning is key to addressing challenges in low-resource languages. “Low-Resource English-Tigrinya MT: Leveraging Multilingual Models, Custom Tokenizers, and Clean Evaluation Benchmarks” by Hailay Kidu at St. Mary’s University demonstrates that custom tokenization and multilingual models improve translation quality for Tigrinya, despite data limitations. This is echoed in “CorIL: Towards Enriching Indian Language to Indian Language Parallel Corpora and Machine Translation Systems” by Soham Bhattacharjee et al., which introduces a massive parallel corpus, highlighting the crucial role of domain-specific data and cross-script transfer learning for Indian languages. A fascinating cognitive-inspired approach, “Attention Schema-based Attention Control (ASAC): A Cognitive-Inspired Approach for Attention Management in Transformers” by Krati Saxena et al. at Alientt, integrates Attention Schema Theory into transformers, leading to more efficient learning, improved generalization, and enhanced resilience to adversarial attacks through effective transfer learning.

Healthcare is another area seeing transformative impact. “Towards Self-Supervised Foundation Models for Critical Care Time Series” by Katja Naasunnguaq Jagd et al. introduces a self-supervised Bi-Axial Transformer (BAT) model for critical care, outperforming supervised baselines for mortality prediction, especially on small datasets. For medical time series, “TS-P²CL: Plug-and-Play Dual Contrastive Learning for Vision-Guided Medical Time Series Classification” by Q. Xu et al. innovatively treats physiological signals as pseudo-images, leveraging pre-trained vision models for robust cross-subject generalization. Further emphasizing interpretability, “From Predictions to Explanations: Explainable AI for Autism Diagnosis and Identification of Critical Brain Regions” by Kush Gupta et al. uses cross-domain transfer learning and XAI techniques to not only diagnose ASD more accurately but also identify critical brain regions, enhancing trust in AI diagnostics.

Engineering and Science also benefit greatly. “A Deep Transfer Learning-Based Low-overhead Beam Prediction in Vehicle Communications” by Xia, Q. et al. proposes deep transfer learning for efficient beam prediction in vehicular networks, addressing dynamic environments with reduced computational overhead. In structural health monitoring, “Model-Based Transfer Learning for Real-Time Damage Assessment of Bridge Networks” by Elisa Tomassini et al. at the University of Perugia introduces a framework that uses neural network surrogate models to transfer knowledge between similar bridge structures for real-time damage assessment. And in a crucial step for clean energy, “Merging Physics-Based Synthetic Data and Machine Learning for Thermal Monitoring of Lithium-ion Batteries: The Role of Data Fidelity” by Yusheng Zheng et al. combines physics-based synthetic data with machine learning to accurately estimate lithium-ion battery internal temperatures, bridging the sim2real gap with domain adaptation.

Under the Hood: Models, Datasets, & Benchmarks

These papers highlight a rich ecosystem of models, datasets, and innovative training strategies:

• Foundation Models & Transformers: The Bi-Axial Transformer (BAT) for critical care time series in “Towards Self-Supervised Foundation Models for Critical Care Time Series” and the Swin Transformer in “Parameter-Efficient Multi-Task Learning via Progressive Task-Specific Adaptation” (code: TGLoRA implementation on GitHub) demonstrate the power of large pre-trained architectures. XL-CoGen, a multi-agent framework in “Beyond Language Barriers: Multi-Agent Coordination for Multi-Language Code Generation”, strategically uses intermediate languages for cross-language code generation.
• Specialized Architectures: DSSCNet in “Enhancing Speaker-Independent Dysarthric Speech Severity Classification with DSSCNet and Cross-Corpus Adaptation” for speech analysis, and R-Net (“R-Net: A Reliable and Resource-Efficient CNN for Colorectal Cancer Detection with XAI Integration”) and S-Net (“A study on Deep Convolutional Neural Networks, transfer learning, and Mnet model for Cervical Cancer Detection”) for medical imaging, highlight tailored CNNs that balance efficiency and high accuracy, often combined with XAI for interpretability. BIGNet from Tsinghua University in “BIGNet: Pretrained Graph Neural Network for Embedding Semantic, Spatial, and Topological Data in BIM Models” offers a novel GNN for Building Information Modeling.
• Data & Evaluation: The CorIL corpus (“CorIL: Towards Enriching Indian Language to Indian Language Parallel Corpora and Machine Translation Systems” code: https://huggingface.co/datasets/HimangY/CoRil-Parallel), and the CUTE dataset (“CUTE: A Multilingual Dataset for Enhancing Cross-Lingual Knowledge Transfer in Low-Resource Languages” code: https://github.com/CMLI-NLP/CUTE) are crucial for advancing low-resource NLP. A new public dataset for partially occluded road signs in “Training A Neural Network For Partially Occluded Road Sign Identification In The Context Of Autonomous Vehicles” supports robust autonomous driving research.
• Techniques & Frameworks: Progressive Task-Specific Adaptation in “Parameter-Efficient Multi-Task Learning via Progressive Task-Specific Adaptation” (code: TGLoRA implementation on GitHub) and Hierarchical Adapter Merging (HAM) in “HAM: Hierarchical Adapter Merging for Scalable Continual Learning” (code: https://github.com/huggingface/peft) are pushing the boundaries of parameter-efficient multi-task and continual learning. MODIS (“MODIS: Multi-Omics Data Integration for Small and unpaired datasets” code: https://github.com/VILLOUTREIXLab/MODIS) introduces a semi-supervised framework for multi-omics data with small, unpaired samples.
• Threats & Monitoring: MAUI (“MAUI: Reconstructing Private Client Data in Federated Transfer Learning”), a data reconstruction attack, highlights privacy concerns in federated transfer learning. Conversely, “Data Quality Monitoring for the Hadron Calorimeters Using Transfer Learning for Anomaly Detection” (code: https://github.com/muleina/CMS_HCAL_ML_OnlineDQM) shows transfer learning’s power in spatio-temporal anomaly detection for scientific instruments.

Impact & The Road Ahead

The collective message from these papers is clear: transfer learning is not just a technique; it’s a foundational principle enabling AI to address complex real-world challenges efficiently and ethically. From robust autonomous systems that can recognize occluded signs to lifesaving medical diagnostics that offer transparency, and from secure and private agricultural AI to efficient quantum chemistry simulations (“SMILES-Inspired Transfer Learning for Quantum Operators in Generative Quantum Eigensolver” by U. Azad and S. Fomichev), the impact is far-reaching.

Future research will likely delve deeper into understanding the underlying mechanisms of transfer, such as the latent traits identified in LLM fine-tuning by “Latent Traits and Cross-Task Transfer: Deconstructing Dataset Interactions in LLM Fine-tuning”, and the universal master key filters in DS-CNNs by Zahra Babaiee et al. in “The Quest for Universal Master Key Filters in DS-CNNs”. The quest for sample-efficiency and generalization in reinforcement learning, as reviewed by Hossein Hassani et al. in “Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review”, remains paramount. Furthermore, integrating cognitive-inspired approaches and ensuring ethical AI deployment through explainability and privacy-preserving methods will shape the next generation of transfer learning applications. The journey of transfer learning continues to unfold, promising a future where AI is more adaptable, reliable, and universally beneficial.