Neural Style Transfer is one of the interesting applications of computer vision using deep learning. In this method, two images named as original content images and the style reference images are blended together by the algorithms. This blending is done in such a way that the resulting image looks like the original content image but painted in the style of the style reference image. This style transfer task is performed 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 the feature extraction capabilities of the convolutional neural network.

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Hands-On guide To Neural Style Transfer using TensorFlow Hub Module
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