Unlike most of the algorithms in deep learning, NST optimizes a cost function to get pixel values. An NST implementation generally uses a pre-trained convolutional network.

It is simply the idea of using a network trained on one task and putting it to use on an entirely new task.

The following are the three component loss functions:

• Content loss
• Style loss
• Total-variation loss

Each component is individually computed and then combined in a single meta-loss function. By minimizing the meta-loss function, we will be, in turn, jointly optimizing the content, style, and total-variation loss as well.