Transfer Learning: Accelerating AI Across Domains, from Genes to Galaxies

Latest 50 papers on transfer learning: Oct. 6, 2025

Transfer learning, the art of leveraging knowledge gained from one task to improve performance on another, is rapidly becoming a cornerstone of modern AI. It’s a pragmatic answer to the pervasive challenge of data scarcity and computational cost, allowing models to adapt quickly and effectively to new, often complex, domains. Recent research showcases not just the power but also the nuanced advancements in this field, pushing the boundaries from medical diagnostics and autonomous systems to low-resource language processing and even quantum computing.

The Big Idea(s) & Core Innovations

The central theme across these papers is the innovative application and theoretical deepening of transfer learning to address real-world bottlenecks. In medical imaging, the field is seeing a surge in specialized frameworks. For instance, the paper “Deep Learning Approaches with Explainable AI for Differentiating Alzheimer Disease and Mild Cognitive Impairment” by Mostafa, Hossain, and Khan proposes a hybrid deep learning ensemble that achieves 99.21% accuracy in distinguishing AD from MCI, leveraging transfer learning from pre-trained architectures. Similarly, Ariel University’s Naomi Fridman and Anat Goldstein, in their work “Transformer Classification of Breast Lesions: The BreastDCEDL AMBL Benchmark Dataset and 0.92 AUC Baseline”, developed a transformer-based framework for breast lesion classification, boasting 100% sensitivity. These approaches highlight how pre-trained models can be exquisitely adapted for high-stakes medical tasks.

Beyond accuracy, efficiency is paramount. The “tCURLoRA: Tensor CUR Decomposition Based Low-Rank Parameter Adaptation and Its Application in Medical Image Segmentation” paper by G. He and W. Cheng (from the National Natural Science Foundation of China and Ministry of Education of China) introduces a parameter-efficient fine-tuning method that significantly reduces trainable parameters while improving segmentation. This addresses the challenge of deploying complex models in resource-constrained environments.

Low-resource domains, particularly in natural language processing and speech recognition, are also seeing major breakthroughs. “CUTE: A Multilingual Dataset for Enhancing Cross-Lingual Knowledge Transfer in Low-Resource Languages” by Wenhao Zhuang and Yuan Sun (Minzu University of China) introduces the largest open-source corpus for Uyghur and Tibetan, validating that machine translation can effectively generate training data for low-resource languages. Building on this, “LTA-L2S: Lexical Tone-Aware Lip-to-Speech Synthesis for Mandarin with Cross-Lingual Transfer Learning” from Jiangnan University and the Institute of Acoustics Chinese Academy of Science, tackles Mandarin lip-to-speech synthesis by using cross-lingual transfer from English models, improving tonal accuracy and intelligibility. The systematic review, “Automatic Speech Recognition (ASR) for African Low-Resource Languages: A Systematic Literature Review”, further underscores the critical need for ethically curated, diverse datasets and linguistically-informed models for African languages.

In the realm of reinforcement learning (RL), the paper “Learning Distinguishable Representations in Deep Q-Networks for Linear Transfer” by OpenAI, Google Research, and DeepMind researchers explores how learning distinguishable representations can improve linear transfer in deep Q-networks, enhancing adaptability across tasks. This is further contextualized by the comprehensive review, “Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review”, which highlights how T-IRL methods improve sample efficiency and generalization through human-in-the-loop and sim-to-real strategies.

Beyond these core areas, transfer learning is proving its versatility: from detecting smart contract vulnerabilities with Satellite (https://arxiv.org/pdf/2509.23679) by Janelinux, which leverages existing code patterns to identify 14 new types of vulnerabilities, to “SMILES-Inspired Transfer Learning for Quantum Operators in Generative Quantum Eigensolver” by U. Azad and S. Fomichev (University of Toronto, MIT), which uses chemical representations to enhance quantum operator modeling. Even in abstract theoretical work, “Towards Understanding Feature Learning in Parameter Transfer” by Southeast University and University of Michigan researchers, provides the first theoretical framework to analyze parameter transfer dynamics, explaining when and why it benefits or hinders performance.

Under the Hood: Models, Datasets, & Benchmarks

Innovation often stems from new resources or clever utilization of existing ones. Several papers introduce crucial datasets and models:

• BreastDCEDL AMBL Dataset: Introduced in “Transformer Classification of Breast Lesions: The BreastDCEDL AMBL Benchmark Dataset and 0.92 AUC Baseline”, this is the first publicly available DCE-MRI dataset with both benign and malignant lesion annotations. Code is available at https://github.com/naomifridman/BreastDCEDL_AMBL.
• MetaChest Dataset: The paper “MetaChest: Generalized few-shot learning of patologies from chest X-rays” by Berenice Montalvo-Lezama and Gibran Fuentes-Pineda (Universidad Nacional Autónoma de México) presents this large-scale dataset of 479,215 chest X-rays from four public databases, along with ProtoNet-ML for multi-label few-shot learning. Code: https://github.com/bereml/meta-cxr.
• CUTE Dataset: From “CUTE: A Multilingual Dataset for Enhancing Cross-Lingual Knowledge Transfer in Low-Resource Languages”, this 50GB multilingual dataset for Chinese, Uyghur, Tibetan, and English is openly available at https://github.com/CMLI-NLP/CUTE.
• ADPT Framework: “LVLMs as inspectors: an agentic framework for category-level structural defect annotation” by Sheng Jiang et al. introduces this framework that uses LVLMs for automated, zero-shot structural defect annotation with self-questioning refinement.
• TS-P²CL: Presented in “TS-P²CL: Plug-and-Play Dual Contrastive Learning for Vision-Guided Medical Time Series Classification”, this method transforms 1D physiological signals into 2D pseudo-images to leverage pre-trained vision models for medical time series classification.
• TGLoRA: The “Parameter-Efficient Multi-Task Learning via Progressive Task-Specific Adaptation” paper by Neeraj Gangwar et al. (University of Illinois Urbana-Champaign, Amazon) develops TGLoRA, a LoRA-based layer for parameter-efficient multi-task learning. Code available at https://github.com/NeerajGangwar/TGLoRA.
• DSC-SRGM: In “Deep Synthetic Cross-Project Approaches for Software Reliability Growth Modeling”, Kim, Ryu, and Baik (KAIST, Jeonbuk National University) combine synthetic data generation with cross-project transfer learning for software reliability prediction.
• SSTAG: The paper “SSTAG: Structure-Aware Self-Supervised Learning Method for Text-Attributed Graphs” from Institute of Information Engineering, CAS and others introduces SSTAG, which bridges LLMs and GNNs for scalable knowledge transfer on text-attributed graphs.

Impact & The Road Ahead

These advancements herald a future where AI models are more adaptable, data-efficient, and interpretable. The ability to seamlessly transfer knowledge across diverse domains—from medical diagnostics (Alzheimer’s, breast cancer, brain tumors) and critical care time series (https://arxiv.org/pdf/2509.19885) to robust autonomous systems (traffic sign recognition https://arxiv.org/pdf/2503.18177 and vehicle communication https://arxiv.org/pdf/2509.20659)—is transformative. We’re seeing AI systems that can learn from limited data, handle domain shifts, and even incorporate human-aligned judgment in complex decision-making (https://arxiv.org/pdf/2503.02976).

The emphasis on explainable AI (XAI) in medical applications (https://arxiv.org/pdf/2509.16251, https://arxiv.org/pdf/2509.16250) is critical for building trust and enabling clinical adoption. Moreover, the focus on low-resource languages and ethical data curation ensures that AI development is inclusive, rather than perpetuating existing biases. The theoretical work on parameter transfer and the nuanced evaluation of foundation models (https://arxiv.org/pdf/2509.19465) will guide future research, helping practitioners understand the intricacies of transfer learning and avoid pitfalls like negative transfer.

As AI continues its rapid evolution, transfer learning will remain a vital strategy for extending its reach and impact. The future promises more intelligent, generalized, and ethically-aware AI systems, capable of tackling an ever-broader spectrum of challenges with increasing efficiency and insight.