Fine-tuning is a different approach to transfer learning. Both share the same goal of transferring the knowledge learned on a dataset on a specific task to a different dataset and a different task. Transfer learning, as shown in the previous section, reuses the pre-trained model without making any changes to its feature extraction part; in fact, it is considered a non-trainable part of the network.
Fine-tuning, instead, consists of fine-tuning the pre-trained network weights by continuing backpropagation.