Artificial intelligence, the programming that enables machines to think like humans and to mimic their actions. Like face recognition, with artificial intelligence, we can perform many other tasks that assist humans to reduce their work burden. One such most advanced algorithm in machine learning is transfer learning that is discussed in this article.

Human has the power to grasp what they have learned and to transfer it to perform any other activity similar to it.  Like, if you know how to ride a bicycle, then you will feel convenient to learn motorcycles. Similarly, a person who can speak many languages can easily learn many more new languages. These are all real-life examples of transfer learning.

In AI, transfer learning is a category of machine learning that makes it possible to reuse a model, that was developed for a task as a starting point for any model for other task that are similar. In short, we can state it as reusing a pre-trained model for a new problem-solving. Just consider a pre-trained machine learning algorithm that can identify the number of cars passed through a highway by checking the CCTV footage. This same algorithm can be reused for counting any other vehicle like buses or trucks. This is where transfer learning is implemented. In this example, initially, the system was pre-trained to count a number of cars, but a small change made it possible to be used for counting other vehicles. This reduced the effort of development to a greater extent and saved much training time. Few other benefits of transfer learning are listed below.

• Improves baseline performance due to this knowledge transfer
• Brings the power of machine learning to small datasets
• Ability to transfer knowledge across tasks
• Reduces the model development time
• Requires less additional training data
• Makes deep learning more accessible
• Speeds up the process of training the model on a new task

One of the drawbacks of transfer learning is negative transfer. We can implement transfer learning only when the initial and target problems are the same. Otherwise, the transfer learning approach will fail.

There are five types of transfer learning methods like domain adaptation, domain confusion, multitask learning, one-shot learning, and zero-shot learning. The method of domain adaptation is used when the task that needs to be performed is the same but there will be a domain shift or a distribution change between the source and the target. The domain confusion technique is implemented by adding other objectives to the source model. In multitask learning, there will be no distinction between the source and target, and in this method, several tasks are learned simultaneously. One-shot training is another technique utilized and here output is obtained after training just one or a few examples. Zero-shot training is the last one and in this approach, no labeled examples are required for learning the task.