ArticleJournal of big data2022
Transfer learning: a friendly introduction.
Article in Journal of big data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 94 papers, 1 of them a synthesis that pooled it.
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Who cites it
94 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
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- Transformer-Based Multi-Task Learning with a Task-Aware Weighted Ensemble for Simultaneous Age Estimation and Gender Classification from Facial Images.Sensors (Basel, Switzerland) · 2026Article
- Advances in Sensing Techniques and Deep Learning for Food Quality Detection: Opportunities, Challenges, and Perspectives from Image Recognition to Multimodal Data Fusion.Foods (Basel, Switzerland) · 2026Review
- Double shrinkage transfer causal learning: An application to alzheimer's disease.PLoS computational biology · 2026Article
- Opportunities for artificial intelligence and synthetic biology in designing living drug delivery systems.Advanced drug delivery reviews · 2026Review
- SignatureGuard: hybrid CNN-transformer model for signature verification and identification across Arabic and English datasets.Scientific reports · 2026Article
- Foundation for Artificial Intelligence-Driven Democratization of Healthcare Access.Learning health systems · 2026Article
- Leveraging quantum chemical properties in transfer learning for predicting blood-brain barrier permeability of drugs.Drug delivery and translational research · 2026Article
- Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.PLoS computational biology · 2026Article
- A low-cost vision based hand gesture interface for real time industrial motor control in resource constrained environments.Scientific reports · 2026Article
- A Validation-Driven Explainable Deep Ensemble Framework for Image-Based Saffron Adulteration Detection.Foods (Basel, Switzerland) · 2026Article
- Machine learning enhanced optical spectroscopy for breast cancer diagnosis: A review.Lasers in medical science · 2026Review
- Natural Products in Epilepsy Treatment: From Traditional Medicine Towards Computational Drug Discovery.Current issues in molecular biology · 2026Review
- Article
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- Artificial Intelligence in Ocular Surface Tumors: Current Advances, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2026Review
- Utility of multimodal deep learning model to diagnose lymph node metastasis in esophageal cancer using computed tomography and positron emission tomography images.Surgery today · 2026Article
- AI-guided surgical decision support system for corneal laceration repair: a prospective, non-randomized controlled feasibility study.Scientific reports · 2026Article
- How artificial intelligence is shaping neuropsychology: A focus on cognitive assessment of neurodegenerative disorders.Journal of neuropsychology · 2026Review
34 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Infinite numbers of real-world applications use Machine Learning (ML) techniques to develop potentially the best data available for the users. Transfer learning (TL), one of the categories under ML, has received much attention from the research communities in the past few years. Traditional ML algorithms perform under the assumption that a model uses limited data distribution to train and test samples. These conventional methods predict target tasks undemanding and are applied to small data distribution. However, this issue conceivably is resolved using TL. TL is acknowledged for its connectivity among the additional testing and training samples resulting in faster output with efficient results. This paper contributes to the domain and scope of TL, citing situational use based on their periods and a few of its applications. The paper provides an in-depth focus on the techniques; Inductive TL, Transductive TL, Unsupervised TL, which consists of sample selection, and domain adaptation, followed by contributions and future directions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.