Neural Style Transfer (NST)

Neural Style Transfer also known as NST is a deep learning application that can fuse the artistic component of an image with the visual patterns, textures, and colors of another image to generate an image that has never existed before. It’s not similar to physically blending one image over another instead it takes another approach to match the artistic components of one image with the patterns, textures or the colors of another images. As shown in the below image, first image has a dog, and Second image has some patterns, textures and colors. So, the resultant image created by NST was a fusion of both images. A dog in the pattern, texture and color provided in the second image.

At a higher level, NST revolves around Convolutional Neural Networks (CNNs) particularly using pre-trained models like VCG or ResNet that can analyze visuals and employ additional measures to extract style from one image and apply it to another image. At the beginning convolutional layers of pre-trained models are used to extract all the component and style-related information from the provided image.

NST uses a multi-layered architecture in which early layers of architecture capture the low-level features like textures and colors meanwhile the deeper layer of the model is used to capture more complex and abstract features aiming to preserve the structures and the content-specific details.

After all the extractions like Style, Content, Pattern, Texture, Colors, etc. are performed. Image generation starts with the content image or a random noise. Some optimization processes are also employed to adjust the pixel values of the initialized image to match the content representation of the first image and the style representation of the second image.

NST loss function consists of three main components:

• Content Loss (Measure of difference in the content of the generated image and actual image)
• Style Loss (Measure of difference in the style of generated image and the actual image)
• Total Variation loss (It controls the smoothness of the generated image).

A weighted sum of all these losses is used to guide the optimization process to generate an image that is a balance between both the first and second images.

We all are living in an era of Artificial Intelligence and have felt its impact. There are numerous AI tools for various purposes ranging from Text Generation to image Generation to Video Generation to many more things. You must have used text-to-image models like Dall-E3, Stable Diffusion, MidJourney, etc. And it might be that you’re fascinated with their image-generation capabilities as they can generate realistic images of non-existent objects or can enhance existing images. They can convert your imagination into an image in a matter of seconds. But how?

In this article, we are going to explore how all these TTM models have this kind of imagination that can generate images that they’ve never seen.