Hands-On Transfer Learning with Python

Content loss

In any CNN-based model, activations from top layers contain more global and abstract information (for example, high-level structures like a face), and bottom layers will contain local information (for example, low-level structures like eyes, nose, edges, and corners) about the image. We would want to leverage the top layers of a CNN for capturing the right representations for the content of an image. Hence, for the content loss, considering we will be using the pretrained VGG-16 model, we can define our loss function as the L2 norm (scaled and squared Euclidean distance) between the activations of a top layer (giving feature representations) computed over the target image, and the activations of the same layer computed over the ...