This notebook provides a comprehensive guide to implementing the Neural-Style algorithm developed by Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. Known as Neural-Transfer or Neural-Style, this technique allows you to transform an image by infusing it with a new artistic style. The process involves three images: an input image, a content image, and a style image. The algorithm alters the input image to mirror the content of the content image while adopting the artistic style of the style image.

Core Concept

The core concept is simple: we establish two metrics—one for content (D_C) and one for style (D_S). The content distance (D_C) quantifies how different the content is between two images, and the style distance (D_S) assesses how different the styles are. The algorithm processes a third image, the input, and adjusts it to minimize both the content distance from the content image and the style distance from the style image.