Channeling Picasso with Style Transfer and Google's TensorFlow

We are always jumping back and forth between hardware and software upgrades to our painting robot. This week it's the software. Pleased to report that we now have our own implemention of Dumoulin, Shlens, and Kudlar's Style Transfer. This of course is the Deep Learning algorithm that allows you to recreate content in the style of a source painting. 

The first image that we successfully created was made by transferring the style of Picasso's Guernica into a portrait of me in my studio.  

So here are the two images we started with. 

And the following is the image that the neural networks came up with.

I was able to get this neural net working thanks in large part to the step-by-step tutorial in this amazing blog post by LO at Cool thing about the Deep Learning community, is that I found half a dozen good tutorials. So if this one doesn't work out for you, just search out another.

Even cooler though, is that you don't even need to set up your own implementation. If you want to do your own Style Transfers, all you have to do is head on over to the Deep Dream Generator at On this site you can upload pictures and have their implementation generate your own custom Style Transfers.  There is even a way to upload your own source images and play with the settings.  

Below is a grid of images I created on the Deep Dream Generator site using the same content and source image that I used in my own implementation.  In them, I played around with the Style Scale and Style Weight settings. Top row has Scale set to 1.6, while second row is 1, and third is 0.4.  First column has the Weight set to 1, while second is at 5 and third is at 10.

So while I suggest you go through the pains of setting up your own implementation of Style Transfer, you don't even have to.  Deep Dream Generator lets you perform 10 style transfers an hour.

For us on the other hand, we need our own generator as this technology will be closely tied into all robot paintings going forward.