Artificial intelligence techniques have experienced a renaissance in recent years with neural networks, a type of algorithm that is (loosely) modeled after connectionist models of the brain. In this exhibit, we explore the use of generative adversarial networks (GANs) which learn to create visual media by training on large datasets of human-generated images, and then output novel images when fed a random vector input, generating the image ex nihilo. Experience the progression of images generated by GANs from their early nascent stages as pixelated creatures through their turbulent adolescent times before reaching a tentative mature adulthood that you see at the end.
Catherine Hong is a high-school senior interning at the Imaging Lyceum at Arizona State University. Her research interests involve art and artificial intelligence in the form of machine learning. In her spare time, she enjoys creating art (sans GAN).
Suren Jayasuriya is an assistant professor in the School of Arts, Media and Engineering and the School of Electrical, Computer and Energy Engineering. His research interests are in computational imaging and photography, computer vision and graphics, and image sensors.
Albert Reed is a PhD student with the School of Electrical, Computer and Energy Engineering at Arizona State University. His research interests are machine learning and signal processing.
Nathan Harness is an undergraduate student in the Fulton Schools of Engineering at Arizona State University, working on GANs for image generation.