Neural artistic style transfer experiments with Keras

Fork Artistic style transfer using neural networks is a technique proposed by Gatys, Ecker and Bethge in the paper: arXiv:1508.06576 [cs.CV] which exploits a trained convolutional network in order to reconstruct the elements of a picture adopting the artistic style of a particular painting. I’ve written a Python program (available in the Github repository: https://github.com/giuseppebonaccorso/Neural_Artistic_Style_Transfer) based on Keras and VGG16/19 convolutional networks, that can be used to perform some experiments. In fact, considering the huge number of variables and parameters, this kind of problems is very sensitive to the initial conditions and a different starting state can lead to different minima which content doesn’t meet our requirements. In the script, it’s possible to choose among six initial canvas types: Random: RGB random pixels from a uniform distribution Random from style: random pixels sampled from the painting Random from picture: random pixels sampled from the picture Style/Picture: Painting or picture full […]