Transfer learning is a technique where knowledge gained from one task is leveraged to solve another similar task.
Imagine a model that is trained on millions of images that span thousands of classes of objects (not just cats and dogs). The various filters (kernels) of the model would activate for a wide variety of shapes, colors, and textures within the images. Those filters can now be reused to learn features on a new set of images. Post learning the features, they can be connected to a hidden layer prior to the final classification layer for customizing on the new data.
ImageNet (http://www.image-net.org/) is a competition hosted to classify approximately 14 million images into 1,000 different classes. It has a variety of classes in the dataset, including Indian elephant, lionfish, hard disk, hair spray, and jeep.
The deep neural network architectures that we will go through in this chapter have been trained on the ImageNet dataset. Furthermore, given...