Before deep learning arrived at the scene, researchers had been handcrafting methods to extract the content and texture of images, merge them and see if the results were interesting or garbage. Put another way, the central problem of style transfer revolves around our ability to come up with a clear way of computing the "content" of an image as distinct from computing the "style" of an image. This definition of style transfer might seem a bit imprecise. To work properly we need a way to (1) determine the content and the style of any image (content/style extractor) and then (2) merge some arbitrary content with another arbitrary style (merger). The original insight of using deep learning in this areaĪccording to the academic literature, image style transfer is defined as follow: given two images on the input, synthesize a third image that has the semantic content of the first image and the texture/style of the second.In this article we will do a deep dive into how style transfer works and then use the power of deep learning style transfer to generate our own images. The technology that generates these images is also quite remarkable from a technology perspective and is worth understanding. The figure below shows an example using one photo of the author and the famous painting "The Scream" by Edvard Munch.Īs we can imagine, this technique has tremendous potential - it gives anyone the power to produce beautiful artwork, inspired by their favorite paintings, textures and etc. The main idea behind style transfer is to take two images, say, a photo of a person, and a painting, and use these to create a third image that combines the content of the former with the style of the later. Central to this discussion is the recent advances in image style transfer using deep learning. One of the most interesting discussions today around within machine learning is how it might impact and shape our cultural and artistic production in the next decades. Introduction to image style transfer using deep learning
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