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In general, we will have a lot of data and we train the weights by minimizing the loss function.
Unlike other deep learning methods, here we use pre-trained weights which were trained on millions of images and update the pixel values to get the stylized image.
Thus, neural style transfer is an optimization technique used to take two images—a content image and a style reference image (such as an artwork by a famous painter)—and blend them together so the output image looks like the content image, but “painted” in the style of the style reference image.
This is implemented by optimizing the output image to match the content statistics of the content image and the style statistics of the style reference image. These statistics are extracted from the images using a convolutional network.
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