Neural style transfer uses deep neural networks to extract and combine the content and styles of two images.
Typically, a pretrained convolutional neural network (CNN) is used, which represents different features such as structure (content) and texture (style) on various layers.
The content of a source image and the style of a target image are evaluated separately through loss functions, for example content loss and style loss.
Through iterative optimization, a new image is generated that combines the content of the source image with the style of the target image.
This technique is applied in art, image editing, and creative AI.