There has been an explosion of interest in generating art by effecting style transfer through generative adversarial networks. I have been showing AI-generated art in New York art galleries and in this talk, I want to share how I explore the aesthetic effect produced by different network configurations as determined by choice of hyperparameters, depth of intermediate layers or strength of the transfer effect.
My training set is a mixed corpus drawn from both my own work (abstract photography and oil paintings), that I have shown publicly in the last year as well as representative samples of older prominent art movements including Cubism, Impressionism and Abstract Expressionism.
In this Q&A, I step away from the mechanistic aspect of generation and focus instead on the aesthetic implications of design choices made in the training phase.