Transfer learning is one of the popular approach in deep learning where a model developed for one task is reused for another model on a different task. Here pre-trained models are used as a first step on computer vision based tasks or natural language processing (NLP) based tasks provided we have very limited computational resources and time.
In a typical computer vision based problem, neural networks try to detect edges in their initial level layers, shapes in the middle level layers and more specific features in the final level layers. With transfer learning, we will use the initial and middle level layers and only re-train the final level layers.
For example, if we have a model trained to recognize an apple from the input image, it will be reused to detect water bottles. In the initial layers, the model has been trained to recognize objects so...
