Process of Style Transferring
Neural style transfer is an optimization technique that is used for two images – a content image and a style reference image – and they are merged so that the output image looks like a content image, but in the style of the style reference image “depicted” is.
To achieve style transfer, it is necessary to separate the style of the image from its content. After that, it is possible to transfer the style elements of one image to the content elements of another image. This process is mainly carried out using the feature extraction form standard nonlinear neural network.
These features are then manipulated to extract content information or style information. The process involves three images a style image, a content image, and finally, a target image.
The ultimate goal is the style of the style image combined with the content of the content image to create the image.
This process begins by selecting some layers from within our model to extract features. We have a good idea of how our image is processed in neural networks by choosing a few layers to extract features. We remove the model attributes of our style image and content image. After that, we remove the elements from our target image and compare it with our style image feature and our content image feature.