Definition
In transfer learning in AI, the knowledge of an already trained machine learning model is been put to use in a different but related task. The idea behind the concept is to use the knowledge a model has gained from a task with a lot of available labeled training data, in a new task that doesn’t have much data. Instead of starting the learning process from scratch, we start with patterns learned from solving a related task [1]. For example, imagine a language translation app that initially learns English. When it wants to learn Yoruba, instead of starting from scratch, it uses its existing English language understanding as a foundation.
Origin
The roots of
transfer learning in AI can be traced back to the early 1990s, where
researchers began exploring ways to leverage knowledge from one domain to solve
related problems in another, evolving into sophisticated methodologies as data
volumes expanded.
Context and Usage
Transfer learning is an emerging technology that is being applied in various fields of machine learning. It is already been put into use in industries such as autonomous driving, gaming industry, healthcare sector, emails, and ecommerce industry [3].
Why it Matters
At its heart,
transfer learning is essentially a design approach to enhancing efficiency.
Instead of training models from scratch, which requires a vast amount of
resources, money, and time in the case of cutting-edge generative AI models, transfer
learning allows models to learn more quickly and effectively in new tasks by
leveraging the knowledge acquired in the past.
Transfer learning shines when little data is available to train a model in a second task. By using the knowledge of a pre-trained model, transfer learning can help prevent overfitting and increase overall accuracy [4].
Related Terms
- Fine-Tuning: This is adapting a pre-trained model to a specific task or dataset by further training it on a smaller, more targeted dataset, allowing it to perform better on that particular task while retaining its original capabilities
- Feature Extraction: This is the process of transforming raw data into a set of meaningful characteristics (features) that can be used to train models and improve their performance.
- Domain Adaptation: This is the process of adapting a model trained on one "source domain" (a specific dataset or environment) to perform well on a different, but related, "target domain" (another dataset or environment) where labeled data might be scarce or unavailable.
In Practice
A real-life case
study of Transfer Learning in AI been practiced can be seen in the case of Google
Translate, which uses transfer learning to improve translations across multiple
languages, including Nigerian languages like Yoruba, Igbo, and Hausa, by
leveraging knowledge from one language to another. This approach saves time,
reduces the amount of training data needed, and makes language learning for AI
much more efficient.
References
- Donges, N. (n.d). What Is Transfer Learning? Exploring the Popular Deep Learning Approach.
- Geeksforgeeks. (2025). What is Transfer Learning?
- Kanade, V. (2025). What Is Transfer Learning? Definition, Methods, and Applications.
- Lunar, J., C. (2024). What is Transfer Learning in AI? An Introductory Guide with Examples.