Understanding how to transfer style between images

In Chapter 2, Deep CNN Architectures, we discussed convolutional neural networks (CNNs) in detail. CNNs are some of the most successful models when working with image data on tasks such as image classification and object detection, among others. 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 while extracting 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 2, Deep CNN Architectures. We are going to use the convolutional part of the VGG model (excluding the linear layers) to extract content-related features from an image.

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