Neural style transfer

Class of software algorithms for image transformation

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Definition

Software algorithms that manipulate digital images to adopt the visual style of another image using deep neural networks

Common Uses

Creating artificial artwork by transferring styles from famous paintings to user-supplied photographs

Origins

First published in the paper "A Neural Algorithm of Artistic Style" by Leon Gatys et al. in 2015

Neural Style Transfer (NST) is a technique that combines the content of one image with the style of another image to generate a new image that captures the essence of both sources. This technique has been used to create stunning artwork, such as blending the style of a Van Gogh painting with a real-world photograph.

How Neural Style Transfer Works

NST uses a pre-trained convolutional neural network (CNN) to extract the content and style features from the input images. The CNN is trained on a large dataset of images, such as ImageNet, and is able to capture the important characteristics of an input image.The process of NST involves the following steps:

  1. Choose the content image and the style image: The content image is the image that you want to transfer the style to, and the style image is the image that you want to transfer the style from.
  2. Load a pre-trained CNN: A pre-trained CNN, such as VGG-19, is loaded and used to extract the content and style features from the input images.
  3. Define the loss functions: Two loss functions are defined: the content loss function and the style loss function. The content loss function measures the difference between the features of the generated image and the features of the content image. The style loss function measures the difference between the features of the generated image and the features of the style image.
  4. Optimize the loss functions: The total loss function is defined as the weighted sum of the content and style loss functions. The optimizer, such as Adam or LBFGS, is used to minimize the total loss function.
  5. Generate the output image: The output image is generated by optimizing the total loss function. The output image is a combination of the content of the content image and the style of the style image.

Applications of Neural Style Transfer

NST has been used in various applications, including: