additionallynote that running this style-transfer algorithm is slow but the transformation operated by the setup is simple enough that it can be learned by smallfast feedforward convnet as well--as long as you have appropriate training data available fast style transfer can thus be achieved by first spending lot of compute cycles to generate input-output training examples for fixed style-reference imageusing the method outlined hereand then training simple convnet to learn this style-specific transformation once that' donestylizing given image is instantaneousit' just forward pass of this small convnet wrapping up style transfer consists of creating new image that preserves the contents of target image while also capturing the style of reference image content can be captured by the high-level activations of convnet style can be captured by the internal correlations of the activations of different layers of convnet hencedeep learning allows style transfer to be formulated as an optimization process using loss defined with pretrained convnet starting from this basic ideamany variants and refinements are possible licensed to