Generating Diverse Content via Latent Space Illumination
Generative adversarial networks (GANs) are now a ubiquitous approach to procedurally generate content. While GANs can output content that is stylistically similar to human-authored examples, human designers often want to explore the generative design space of GANs to extract content that is diverse with respect to measures of interest. In this talk, I show how searching the latent space of GANs with quality diversity algorithms results in the automatic generation of complex, diverse and realistic content, including faces, video game levels and environments for human-agent coordination.
Stefanos Nikolaidis is an Assistant Professor of Computer Science at the University of Southern California and leads the Interactive and Collaborative Autonomous Robotics Systems (ICAROS) lab. His research focuses on stochastic optimization approaches for learning and evaluation of human-robot interactions. His work leads to end-to-end solutions that enable deployed robotic systems to act optimally when interacting with people in practical, real-world applications. Stefanos completed his PhD at Carnegie Mellon's Robotics Institute and received an MS from MIT, a MEng from the University of Tokyo and a BS from the National Technical University of Athens. His research has been recognized with an oral presentation at NeurIPS and best paper awards and nominations from the IEEE/ACM International Conference on Human-Robot Interaction, the International Conference on Intelligent Robots and Systems, and the International Symposium on Robotics.