Fork I’ve just moved my Keras-based Neural Artistic Style Transfer GIST to a dedicated repository: https://github.com/giuseppebonaccorso/Neural_Artistic_Style_Transfer. Please refer always to it because the GIST is not more maintained. See also: Neural artistic style transfer experiments with Keras – Giuseppe Bonaccorso 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.
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 […]
(Updated on July, 24th, 2017 with some improvements and Keras 2 style, but still a work in progress) CIFAR-10 is a small image (32 x 32) dataset made up of 60000 images subdivided into 10 main categories. Check the web page in the reference list in order to have further information about it and download the whole set. Considering our current screen resolutions, it’s not difficult saying that those images are no more than icons and indeed some of them are very hard to be classified even by human beings. Using Keras, I’ve modeled a deep convolutional network (VGGNet-like) in order to try a classification. I’m still investigating the best architecture (in CIFAR home page, there are very interesting references to papers and other results), however, I think it can be a good starting point. As the output is a softmax layer, it can also be interesting to evaluate mixed […]