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Neural Style Transfer and Visualization of Convolutional Networks

Neural Style Transfer and Visualization of Convolutional Networks

Likewise, we admire the story of musicians, artists, writers and every creative human because of their personal struggles, how they overcome life’s challenges and find inspiration from everything they’ve been through. That’s the true nature of human art. That’s something that can’t be automated, even if we achieve the always-elusive general artificial intelligence. — Ray Dickson, BD TechTalks Neural style transfer using the style of famous “ Great Wave off Kanagawa ” and transferring to the skyline of Chicago.

This article will be a tutorial on using neural style transfer (NST) learning to generate professional-looking artwork like the one above. NST has been around for a while and there are websites that perform all of the functions before you, however, it is very fun to play around and create your own images. NST is quite computationally intensive, so in this case, you are limited not by your imagination, but primarily by your computational resources.

All the code used in this article is available on a Jupyter notebook provided on my Neural Networks GitHub page. By the end of this article, you will have all the resources necessary to generate your own work using any images.

Artistic generation of high perceptual quality images that combines the style or texture of some input image, and the elements or content from a different one.

From the above definition, it becomes clear that to produce an image using NST we require two separate images. The first image is one that we wish to transfer the style of — this could be a famous painting, such as the “ Great Wave off Kanagawa ” used in the first image we saw. We then take our second image and we transform this image using the style of the first image in order to morph the two images.

This is illustrated in the images below, where image A is the original image of a riverside town, and the second image (B) is after image translation (with the style transfer image shown in the bottom left).

Source: towardsdatascience.com

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