Published on: April 2026
TRANSFER LEARNING IN DATA SCIENCE: A COMPREHENSIVE REVIEW
Dr. Ajay Singh Thakur
Article Status
Available Documents
Abstract
This article offers an extensive overview of transfer learning, discussing its basic principles, categories, techniques, and practical uses in fields like computer vision, natural language processing, healthcare, and finance (Hosna et al., 2022). It also explores essential methods such as instance-based, feature-based, and parameter-based transfer learning strategies, which enable efficient knowledge transfer between domains (Pan & Yang, 2010). Additionally, the assessment emphasizes the benefits of transfer learning, including enhanced model precision, shorter training durations, and more effective resource use, while also addressing issues such as adverse transfer, domain misalignment, and the interpretability of models (Duhok & Abdulazeez, 2024).
How to Cite this Paper
Thakur, A. S. (2026). Transfer Learning in Data Science: A Comprehensive Review. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.244
Thakur, Ajay. "Transfer Learning in Data Science: A Comprehensive Review." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.244.
Thakur, Ajay. "Transfer Learning in Data Science: A Comprehensive Review." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.244.
Search & Index
References
- Duhok, A. H. A., & Abdulazeez, A. M. (2024). Transfer learning in machine learning: A review of methods and applications. Indonesian Journal of Computer Science.
- Hosna, A., Merry, E., Gyalmo, J., Alom, Z., Aung, Z., & Azim, M. A. (2022). Transfer learning: A friendly introduction. Journal of Big Data, 9(102). https://doi.org/10.1186/s40537-022-00652-w
- Ibrahim, F. (2023). Transfer learning in data science: Reviewing transfer learning techniques. African Journal of Artificial Intelligence and Sustainable Development.
- Long, M., Cao, Y., Wang, J., & Jordan, M. I. (2015). Learning transferable features with deep adaptation networks. In Proceedings of the 32nd International Conference on Machine Learning (ICML) (pp. 97–105).
- Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. https://doi.org/10.1109/TKDE.2009.191
- Ruder, S. (2019). Neural transfer learning for natural language processing (Doctoral dissertation). National University of Ireland.
- Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., & Liu, C. (2018). A survey on deep transfer learning. In International Conference on Artificial Neural Networks (ICANN) (pp. 270–279). Springer.
- Weiss, K., Khoshgoftaar, T. M., & Wang, D. (2016). A survey of transfer learning. Journal of Big Data, 3(9). https://doi.org/10.1186/s40537-016-0043-6
- Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? In Advances in Neural Information Processing Systems (NeurIPS) (pp. 3320–3328).
- Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., & He, Q. (2021). A comprehensive survey on transfer learning. Proceedings of the IEEE, 109(1), 43–76. https://doi.org/10.1109/JPROC.2020.3004555
Ethical Compliance & Review Process
- •All submissions are screened under plagiarism detection.
- •Review follows editorial policy.
- •Authors retain copyright.
- •Peer Review Type: Double-Blind Peer Review
- •Published on: Apr 11 2026
This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.