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An Intuition of Neural Style Transfer

Part 1 on Neural Style Transfer

Neural Style Transfer deals with two sets of images: Content image and Style image. It recreates the content image in the style of the Style image.

Here are the required inputs to the model for image style transfer:

1. A Content Image –an image to which we want to transfer style to
2. A Style Image — the style we want to transfer to the content image
3. An Generated Image— the final blend of content and style image

NST employs a pre-trained Convolutional Neural Network with added loss functions to transfer style from one image to another and synthesize a newly generated image with the features we want to add.

With deep CNN, we meticulously segregate the representations of content and style. In this context, the VGG network emerges as a prominent player due to its remarkable ability in constructing robust semantic representations.

It is our feature extractor.

To extract the essence of content representation, we execute the following steps:

1. We employ diverse images as input through VGG and selectively choose feature maps from a designated layer.
2. These feature maps intricately capture…