{"id":1330,"date":"2025-09-29T07:56:48","date_gmt":"2025-09-29T07:56:48","guid":{"rendered":"https:\/\/scipapermill.com\/index.php\/2025\/09\/29\/transfer-learning-bridging-gaps-and-boosting-performance-across-ais-frontier\/"},"modified":"2025-12-28T22:05:21","modified_gmt":"2025-12-28T22:05:21","slug":"transfer-learning-bridging-gaps-and-boosting-performance-across-ais-frontier","status":"publish","type":"post","link":"https:\/\/scipapermill.com\/index.php\/2025\/09\/29\/transfer-learning-bridging-gaps-and-boosting-performance-across-ais-frontier\/","title":{"rendered":"Transfer Learning: Bridging Gaps and Boosting Performance Across AI’s Frontier"},"content":{"rendered":"

Latest 50 papers on transfer learning: Sep. 29, 2025<\/h3>\n

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.<\/p>\n

The Big Idea(s) & Core Innovations<\/h3>\n

Many recent advances in transfer learning focus on adaptability and efficiency<\/em>, enabling models to perform complex tasks with less data and fewer parameters. For instance, in computer vision<\/strong>, the paper \u201cAdversarial Robustness of Discriminative Self-Supervised Learning in Vision<\/a>\u201d by \u00d6mer Veysel \u00c7a\u011fatan et al.\u00a0at Ko\u00e7 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, \u201cCross-Domain Underwater Image Enhancement Guided by No-Reference Image Quality Assessment: A Transfer Learning Approach<\/a>\u201d 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.<\/p>\n

In natural language processing (NLP)<\/strong>, transfer learning is key to addressing challenges in low-resource languages. \u201cLow-Resource English-Tigrinya MT: Leveraging Multilingual Models, Custom Tokenizers, and Clean Evaluation Benchmarks<\/a>\u201d by Hailay Kidu at St.\u00a0Mary\u2019s University demonstrates that custom tokenization and multilingual models improve translation quality for Tigrinya, despite data limitations. This is echoed in \u201cCorIL: Towards Enriching Indian Language to Indian Language Parallel Corpora and Machine Translation Systems<\/a>\u201d 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, \u201cAttention Schema-based Attention Control (ASAC): A Cognitive-Inspired Approach for Attention Management in Transformers<\/a>\u201d by Krati Saxena et al.\u00a0at Alientt, integrates Attention Schema Theory into transformers, leading to more efficient learning, improved generalization, and enhanced resilience to adversarial attacks through effective transfer learning.<\/p>\n

Healthcare<\/strong> is another area seeing transformative impact. \u201cTowards Self-Supervised Foundation Models for Critical Care Time Series<\/a>\u201d by Katja Naasunnguaq Jagd et al.\u00a0introduces 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, \u201cTS-P2<\/sup><\/span>CL: Plug-and-Play Dual Contrastive Learning for Vision-Guided Medical Time Series Classification<\/a>\u201d by Q. Xu et al.\u00a0innovatively treats physiological signals as pseudo-images, leveraging pre-trained vision models for robust cross-subject generalization. Further emphasizing interpretability, \u201cFrom Predictions to Explanations: Explainable AI for Autism Diagnosis and Identification of Critical Brain Regions<\/a>\u201d by Kush Gupta et al.\u00a0uses 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.<\/p>\n

Engineering and Science<\/strong> also benefit greatly. \u201cA Deep Transfer Learning-Based Low-overhead Beam Prediction in Vehicle Communications<\/a>\u201d by Xia, Q. et al.\u00a0proposes deep transfer learning for efficient beam prediction in vehicular networks, addressing dynamic environments with reduced computational overhead. In structural health monitoring, \u201cModel-Based Transfer Learning for Real-Time Damage Assessment of Bridge Networks<\/a>\u201d by Elisa Tomassini et al.\u00a0at 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, \u201cMerging Physics-Based Synthetic Data and Machine Learning for Thermal Monitoring of Lithium-ion Batteries: The Role of Data Fidelity<\/a>\u201d by Yusheng Zheng et al.\u00a0combines physics-based synthetic data with machine learning to accurately estimate lithium-ion battery internal temperatures, bridging the sim2real gap with domain adaptation.<\/p>\n

Under the Hood: Models, Datasets, & Benchmarks<\/h3>\n

These papers highlight a rich ecosystem of models, datasets, and innovative training strategies:<\/p>\n