I’m continuing my adventure of making images using GANs, a machine learning technique that uses two neural networks trained on a large number of images to produce new examples. This week I made some new portraits.
This week I was reminded of the story of the happy face…recalling that it predates the whole concept of emoticons. I remember being told that this is the simplest drawing that a newborn will respond to.
Whether or not that’s true, we’re so obviously hard-wired to notice faces. They jump out at you, even when they’re not really faces, and I guess you don’t have to think too hard about our evolution to imagine why it would have been important to all of our ancestors to notice faces and pay attention to their nuances.
Years ago I tried photographing inanimate objects in studio lighting conditions – photographing, say, a fire hydrant with a softbox as key, a fill and a rim light. I thought it would look interesting. It mostly just looked like a fire hydrant. What always does look interesting, though, is a picture of a person. Maybe that’s why the first images I made using components from various GANs that really kind of worked were pictures of people:
I used a DCGAN to make faces based on several thousand faces from Renaissance paintings. The body came from Robbie Barrat’s GAN pre-trained on portrait paintings, with some transfer learning from my renaissance dataset. I used SRGAN to increase the resolution (by inventing detail), and a style transfer GAN to help blend the face with the style of the body.
It’s hard to credit the full lineage of researchers and artists who had a hand in building these tools, much less acknowledging the nearly 20,000 paintings that went into training the GANs for making the images.
This is not a finished image – I am doing some gilding and incorporating background details in this, but it’s interesting to note that this image stands on its own better than the more symbolic and abstract pieces that I’m doing like the one in the previous blog post. I still don’t have that one at a point that I’m happy with.