Understanding how to transfer style between images
In Chapter 3, Deep CNN Architectures, we discussed convolutional neural networks (CNNs) in detail. CNNs are largely the most successful class of models when working with image data. We have seen how CNN-based architectures are the best-performing architectures of neural networks on tasks such as image classification, object detection, and so on. One of the core reasons behind this success is the ability of convolutional layers to learn spatial representations.
For example, in a dog versus cat classifier, the CNN model is essentially able to capture the content of an image in its higher-level features, which helps it detect dog-specific features against cat-specific features. We will leverage this ability of an image classifier CNN to grasp the content of an image.
We know that VGG is a powerful image classification model, as discussed in Chapter 3, Deep CNN Architectures. We are going to use the convolutional part of the VGG...