Transfer Learning

Structure of transfer learning

Transfer learning is the application of knowledge gained from completing one task to help solve a different but related problem. We discussed the different types of weight initialization. How about we use already trained and prepared weights to initialize our model? That’s the exact logic of transfer learning.

Imagine that we already have a model trained to predict cats and dogs, now we need another model to classify birds and horses. Instead of training a new model from scratch, we can use this previous model already learned how to extract significant features like legs, eyes, and many more cat and dog classes.