Since this chapter and book is focused upon transfer learning, let's quickly get on with the actual task of leveraging and transferring learned information. We have discussed different state-of-the-art CNN architectures in the previous section. Let's now leverage the VGG-16 model, trained on ImageNet, to classify images from the CIFAR-10 dataset. The code for this section is available in the IPython Notebook CIFAR10_VGG16_Transfer_Learning_Classifier.ipynb.
ImageNet is a huge visual dataset with over 20,000 different categories. CIFAR-10, on the other hand, is restricted to only 10 non-overlapping categories. A powerful network like VGG-16 requires immense computational power and time to train to perform better than ...
