The reusability of code is one of the fundamental concepts of object-oriented programming (OOP) and it is pretty popular in the software-engineering world. Similarly, transfer learning involves reusing a model built to achieve a specific task to solve another related task.
It is understandable that to achieve better performance measurements, ML models need to be trained on large amounts of labeled data. The availability of fewer amounts of data means less training and the result is a model with suboptimal performance.
Transfer learning attempts to solve the problems arising from the availability of fewer amounts of data by reusing the knowledge obtained by a different related model. Having fewer data points available to ...
