In Chapter 1, Exploring the Machine Learning Landscape, we touched upon a concept called transfer learning. The idea is to take the knowledge learned in a model and apply it to another related task. Transfer learning is used on almost all computer vision tasks nowadays. It's rare to train models from scratch unless there is a huge labeled dataset available for training.
Generally, in computer vision, CNNs try to detect edges in the earlier layers, shapes in the middle layer, and some task-specific features in the later layers. Irrespective of the image to be detected by the CNNs, the function of the earlier and middle layers remains the same, which makes it possible to exploit the knowledge gained by a pretrained model. With transfer learning, we can reuse the early and middle layers and only retrain the later layers. It...
