Introduction to Transfer Learning
Transfer learning is a machine learning technique in which a model trained on one task is repurposed on another related activity. So basically it is the reuse of a previously learned model on a new problem known as transfer learning. Transfer learning is an optimization technique that allows rapid improved performance when modeling the second task.Nevertheless, transfer learning is famous in deep learning given the enormous resources required to train deep learning models or the large and challenging datasets on which deep learning models are trained.It is important to highlight that transfer learning is only effective in deep learning when the model features learnt from the first assignment are generic.
Two common approaches are as follows:
This second type of transfer learning method is most famous in the field of deep learning.
Transfer learning is an optimization for saving time or getting better performance. Also there is no guarantee that there will be a benefit in using transfer learning in the domain until after the model has been developed and evaluated first.
The three potential advantages to look for when applying transfer learning:
Higher start. The source model's initial skill (prior to refinement) is higher than it would otherwise be.
Slope is higher. The rate of skill improvement during source model training is higher than it would otherwise be.
Increased asymptote. The trained model has a higher converged skill than it would have otherwise.
A great transfer learning application should provide you with all three benefits.
It is a useful strategy to try if you can discover a relevant task with some data and you have the resources to construct a model for that work and reuse it on your own problem, or a pre-trained model is available that you can use as a starting point for your own model.
On some of the problems where you may not have very much data, transfer learning can enable you to develop skillful models that you are simply unable to develop in the absence of transfer learning.
The choice of source data or source model is an open problem and may require domain expertise that is developed via experience.
Hence Transfer learning is one of the important strategies in the deep learning field especially when we have very little data to train.