Neural Style Transfer

Neural style transfer is an optimization technique that involves taking two images, a content image and a style reference image, and blending them. It builds on the idea that you could separate style representations and content representations in a convolutional neural network learned while performing a computer vision task like an image recognition task.

What is Neural Style Transfer Services?

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.

Use of neural style transfer

Neural style transfer is used in photo editing. It goes beyond your typical Instagram filters where transformations are only applied in the color space. The transformations in neural style transformation are far more sophisticated and elaborate. They aren’t just limited to color.

Content Loss in Neural Style Transfer

Content loss makes sure that the content that you want in the generated image is captured efficiently. Convolution neural networks capture information about the content in the higher levels of the network, while the lower levels tend to focus more on the individual pixel values. It is based on the intuition that images that have similar content will also have similar representation in the higher layers of the network.

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