Traditionally, learning algorithms are designed to tackle tasks or problems in isolation. Depending upon the requirements of the use case and data at hand, an algorithm is applied to train a model for the given specific task. Traditional machine learning (ML) trains every model in isolation based on the specific domain, data and task as depicted in the following figure:
Transfer learning takes the process of learning one step further and more inline with how humans utilize knowledge across tasks. Thus, transfer learning is a method of reusing a model or knowledge for another related task. Transfer learning is sometimes also considered as an extension of existing ML algorithms. Extensive research and work is being done in the context of transfer learning and on understanding how knowledge can be transferred among tasks...
