Neural Style Transfer

Neural Style Transfer

What is Neural Style Transfer?

Neural style transfer is an optimization technique that requires to take three images as its input, a content image, a method reference image (such as an artwork by a famous painter), furthermore the input image you would like to style — and blend them such the input image is transformed to seem just like the content image more percentage of style transfer can be controlled by setting a threshold value.

For example, let’s take a picture of this turtle, and Katsushika Hokusai’s good background Kanagawa:

Image for postImage of Green Sea Turtle by P. Lindgren, from Wikimedia Commons

Output generated by neural style transfer will look like the following image

How does it work?

Unsurprisingly there are not many approaches leading towards NST. The base idea on which Neural Style Transfer is proposed is “it is feasible to separate the design representation and content representations from original image with the help of CNN, learned during a computer vision task and use that data to get a image in desired style ”

we define a pre-trained convolutional model and loss functions that blend two images visually; therefore, we might be requiring the subsequent inputs

A Content Image – image on which we’ll transfer style

A Style Image – the design we would like to transfer

An Input Image(generated) – the ultimate content plus the specified style image

Loss Functions

To get the specified image, we’ll need to define a loss function that can optimize the selected result’s losses. we’ll be using the concept of per pixel losses.

Per Pixel Loss may be a metric that’s wont to understand the differences between images on a pixel level. It compares the output pixel values with the input pixel values. (Another method is perpetual loss functions we’ll discuss briefly at the later stages of the blog). Sometimes per pixel loss has drawbacks in terms of representing every important characteristic of the image. That’s where perpetual losses inherit the image. The term content loss and elegance loss comes into light. These losses are often used for customising the output image. Users can decide the quantity of favor to be transferred from the input image while creating the output image.

Content Loss-Then the content loss is defined as a content loss. Essentially L_{content} captures the basis mean squared error between the activations produced by the generated image and, therefore, the content image.

Style Loss -The style loss is where the deep learning keeps in –that one is defined employing a deep convolutional neural network. Precisely, it consists of a sum of L2 distances between the Gram matrices of the bottom image representations. Therefore, the style reference image, extracted from different convent layers (trained on ImageNet). The overall idea is to capture color/texture information at different spatial scales (relatively large scales –defined by the depth of the layer considered).

Examples of Neural Style Transfer


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