Transfer learning is a technique in machine learning where a model trained on one task is repurposed on a related task, leveraging the knowledge it has already gained to improve learning efficiency and performance.
Transfer learning is a machine learning technique where a pre-trained model is used on a new problem. It's a popular approach in deep learning because it allows us to build accurate models in a timesaving way.
With traditional machine learning, all the learning has to happen when presented with a new task, starting from scratch. However, with transfer learning, the model leverages what it has already learned from previous tasks to improve learning in a new situation.
For example, consider a model that has been trained to recognize various types of furniture, such as chairs, tables, and beds. Now, suppose we want to create a model to identify different types of chairs specifically. Instead of training the model from scratch, we can use the knowledge gained from the furniture recognition model and apply it to our chair-specific model. This is the essence of transfer learning.
Transfer learning is particularly beneficial when you don't have a lot of data for your task. If you were to train your model from scratch with limited data, it might not perform well. However, by leveraging a pre-trained model, you can achieve higher accuracy levels.
For example if you have a self driving car that you have trained to spot other cars. You did this with lots of car pictures. But then, you take your car to a new city where there are lots of motorbikes. Now, you need your car to also spot motorbikes, but you don't have as many pictures of motorbikes as you did of cars. Using transfer learning you can use what your car already knows about detecting cars to help it detect motorbikes. So, even if you don't have many motorbike pictures, your car can still do a good job spotting them. This is because it can use its knowledge of what cars and their parts look like to guess what motorbikes might look like.
Additionally, transfer learning can significantly reduce the computational resources required, since you're not starting the training process from scratch. This can make the development of machine learning models faster and more efficient.
A significant number of computer vision systems employ transfer learning, which greatly enhances their performance.