Introduction of Neural Style Transfer – A Pioneer in Generative AI

Author(s): Vincent Liu

Originally published on Towards AI.

Image source: Photo by Vojtech Bruzek on Unsplash (2nd image)

Q: What can we do with Neural Style Transfer (NST)?

A: Combine multiple images into one by inheriting the content and style from separated images.

If you’re seeking a beginner-friendly guide to Neural Style Transfer, you’ve come to the right place! In this article, we’ll break down the concept of Neural Style Transfer using simple illustrations and minimize the mathematical complexities to ensure easy comprehension.

In computer vision, there is an area called domain adaptation or style transfer which generates a new image by mixing up specific attributes from different images. One may associate these applications with the trending generative models. However, generative models is not a new term and it has come a long way since Generative Adversarial Network (GAN) was published in 2014 [1].

Neural Style Transfer (NST) was born in 2015 [2], slightly later than GAN. It generates a new image by combining the content and style from different images. It is one of the first algorithms to combine images based on deep learning. The main idea of NST is simple:

The content and style of an image can be found in its corresponding feature maps.Generate an image with minimal distances to existing… Read the full blog for free on Medium.

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Published via Towards AI