Adversarial Image Generation Using Evolution and Deep Learning
Jacob Soderlund, Alan Blair · 2018
There has recently been renewed interest in the paradigm of artist-critic coevolution, or adversarial training, in which an artist tries to generate images which are similar in style to a set of real images, and a critic tries to discriminate between the real images and those generated by the artist. We explore a novel configuration of this paradigm, where the artist is trained by hierarchical evolution using an evolutionary automatic programming language called HERCL, and the critic is a convolutional neural network. The system implicitly solves the constrained optimization problem of generating images which have low algorithmic complexity, but are sufficiently suggestive of real-world images as to fool a trained critic with an architecture loosely modeled on the human visual system. The resulting images are not necessarily photorealistic, but often consist of geometric shapes and patterns which remind us of everyday objects, landscapes or designs in a manner reminiscent of abstract art. We explore the coevolutionary dynamics between artist and critic, and discuss possible combinations of this framework with interactive evolution or other human-in-the-loop paradigms.