Artistic Neural Style Transfer with TensorFlow 2.0, Part 2: Implementation

Artistic Neural Style Transfer with TensorFlow 2.0, Part 2: Implementation

This is the second guide in a two-part series on artistic neural style transfer. Part 1 walked through separating the convolution layer for style and content images to extract their respective features. When the loss function is tuned, it combines these features to generate a styled image. This guide, Part 2, will go deeper into style loss and content loss.

This is the second guide in a two-part series on artistic neural style transfer. Part 1 walked through separating the convolution layer for style and content images to extract their respective features. When the loss function is tuned, it combines these features to generate a styled image. This guide, Part 2, will go deeper into style loss and content loss.

Usually, in deep learning, we have only one loss function. However, in neural style transfer, we are generating a new image from two images, so we need more loss functions to generate a new image. We will discuss various loss functions such as content loss, style loss, and variation loss.

There are many approaches to mathematical notation. The equations in this guide are taken from Gatsy et al. (some notations might differ).

Below is a simple representation of how the new image will be generated from the content and style images.

tensorflow

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