What is the role of tensor flow in implementation of style transfer algorithm?
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Answer:
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.
Explanation:
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Answer:
What is Neural Style Transfer?
For new entrants in the computer vision and deep learning field, the term neural style transfer can be a bit overwhelming. To understand each and every component of the term, consider the following two images:
In the context of neural style transfer, the left image is referred to as the content image and the image on the right side is referred to as the style image. You're interested in stylizing one image (the left one in this case) using another image (the right one). This is what constructs the last two words in the term - style transfer. To carry out the process, a neural network (CNN) is trained to optimize a custom loss function, hence the first word - neural. When the above two images are fused using neural style transfer the final output looks like so (right one) -