What is Transfer Learning?
Transfer learning is a machine learning method where a model developed for a task is reused as the starting point for a model on a second task.
Transfer Learning differs from traditional Machine Learning in that it is the use of pre-trained models that have been used for another task to jump start the development process on a new task or problem.
Transfer learning involves the concepts of a domain and a task. A domain DD consists of a feature space XX and a marginal probability distribution P(X)P(X) over the feature space, where X=x1,⋯,xn∈XX=x1,⋯,xn∈X. For document classification with a bag-of-words representation, XX is the space of all document representations, xixi is the ii-th term vector corresponding to some document and XX is the sample of documents used for training.
The benefits of Transfer Learning are that it can speed up the time it takes to develop and train a model by reusing these pieces or modules of already developed models. This helps speed up the model training process and accelerate results.