Hands-On Transfer Learning with Python

Understanding neural style transfer

Neural style transfer is the process of applying the style of a reference image to a specific target image, such that the original content of the target image remains unchanged. Here, style is defined as colors, patterns, and textures present in the reference image, while content is defined as the overall structure and higher-level components of the image.

Here, the main objective is to retain the content of the original target image, while superimposing or adopting the style of the reference image on the target image. To define this concept mathematically, consider three images: the original content (denoted as c), the reference style (denoted as s), and the generated image (denoted as g). We would need ...