What is transfer learning

Machine learning has become a widely-used technique to improve the accuracy of predictive models that are trained on large amounts of data. The technique has wide-ranging applications, including image recognition, natural language processing, and autonomous driving. One of the most important and fast-emerging areas of machine learning is transfer learning. In this article, we will cover what transfer learning is and how it is used in machine learning.

Transfer learning is a method of reusing a pre-trained model (a neural network) that has been trained on a large dataset for a specific task, with the goal of improving performance on a related task. Transfer learning works by adapting the pre-trained model to a new task by fine-tuning the learned features or reusing the pre-trained later layers of the model for the new task.

The concept of transfer learning comes from the idea that when humans learn new things, they usually build on existing knowledge and skills. So, instead of starting from scratch, we can take an existing powerful model that has been trained on a large dataset and retrain it for a new, related problem at a much faster rate, with far fewer examples needed for training compared to training from scratch.

Transfer learning has become increasingly important in the field of machine learning because it helps to overcome some of the challenges of training a model from scratch, such as:

• Limited labeled data: In some domains, it may be difficult or expensive to collect large labeled datasets. Transfer learning enables us to leverage large preexisting labeled data, reducing the need for large labeled training sets.

• Computational cost: Training deep neural networks from scratch can be computationally expensive. Transfer learning allows us to use a pre-trained model, which reduces the amount of time and computational resources required to train a model.

• Generalization performance: Transfer learning helps to produce better generalizations by providing a solid foundation of learned features, allowing models to perform better in new and unseen domains.

How Does Transfer Learning Work?

Using a pre-trained model, the learned features can be employed in the new task either by reusing a subset of the pre-trained model’s layers (partial transfer learning) or fine-tuning the entire pre-trained model by training on the new data and adjusting all its parameters (full transfer learning). The choice of how to use transfer learning will be dependant on the similarity of features required for each task.

Partial transfer learning is used when the pre-trained model’s input domain and output domain is significantly different from that of the new task. The pre-trained model’s layers can be reused and trained using the new data. This means that the internal feature extraction layers of the network are kept whilst retraining the output features required for the new task.

Full transfer learning is used when the pre-trained model is derived from a domain related to the target domain. In this case, the entire pre-trained model can be fine-tuned to the target domain.

Applications of Transfer Learning

Transfer learning has become a widely-used technique in the field of machine learning, with practical applications in various domains including image and video analysis, natural language processing, and speech recognition.

For instance, in image recognition, transfer learning has led to breakthroughs in performing visual recognition tasks with fewer labeled examples, demonstrating accuracy improvements. In natural language processing, researchers fine-tune pre-trained models for related tasks, such as language modeling and sentiment analysis.


Transfer learning is a powerful technique that enables an existing model learned for a particular task to be re-purposed and retrained for a new related task. It helps us to solve problems quicker and with more accuracy than starting from scratch, making it one of the most important emerging areas in machine learning. As more researchers are using transfer learning, we can expect to see more breakthroughs in various domains, and further development of new methods and techniques.