Friday, June 30, 2017

Generation of new artistic styles

A deep learning system that can apply an existing style to a new image is one thing, but can an AI actually generate a new artistic style? These researchers (an assorted group including people from Rutgers and Facebook) and have experimented with using generative adversarial networks to create artwork that is recognized by another neural network as artwork, but doesn't seem to come from any existing style. Personally I like the clouds on the upper right the best, but there are interesting things going on in all the images-- I like how it uses color gradients in particular. It's an interesting new advance, but to feel like art I think one thing this lacks is a motivation for choosing a particular subject. I would like to see a system that makes aesthetic choices in the goal of expressing an idea about the world that comes from having experienced and thought about the world.
The following images generated by the adversarial system were judged by human subjects the most highly in their respective categories (click to enlarge):

Link to the paper

Saturday, June 17, 2017

Wednesday, February 22, 2017

Estimating photos from sketches

When I finished writing Machinamenta six years ago, I suggested some things that could be done to make artificial creativity go beyond simple kaleidoscope patterns. In many ways, deep learning software has surpassed the suggestions I put forward. Here is another example. 
This work comes out of the Berkeley AI research lab at the University of California, Berkeley. Alexei Efros is a familiar name-- he worked on image quilting and automatic photo pop-up and was at CMU (along with Martial Hebert and Abhinav Gupta) during the period I was working with them professionally.
The way this works is that a neural network is trained on pairs of images. The right hand image of the pair is a photograph of a cat; the left hand image is an automatic edge detection on the photograph, using the HED image detector. This means that no humans were needed to create the training data-- important because of how many training samples are needed. It does mean that the edges it is looking for are not necessarily the ones people perceive as most important, but modern edge detectors like HED do a lot better job of that than the Canny edge detector, which was the best available when I first started working on computer vision.
The sketch contains far less information than the photograph. The only reason it is possible to do this at all is that the system has a great prior model of what cats look like, and does its best to fit that model to the constraints of the sketch. I wonder if drawing a Siamese profile would be enough of a hint to give it Siamese coloration? What happens if you try to draw a dog, or a pig, or a house instead?

Try it out yourself, it's a lot of fun.

The original paper