Fall 2024 GRASP SFI: Jun-Yan Zhu, Carnegie Mellon University, “Ensuring Data Ownership in Generative Visual Models”
October 16 at 3:00 PM - 4:00 PM
This will be a hybrid event with in-person attendance in Levine 307 and virtual attendance on Zoom.
ABSTRACT
Large-scale generative visual models have made content creation as little effort as writing a short text description. However, these models are typically trained on an enormous amount of Internet data, often containing copyrighted material, licensed images, and personal photos. How can we remove these images if creators decide to opt out? How can we properly compensate them if they choose to opt in?
In this talk, I will first describe an efficient method for removing copyrighted materials, artistic styles of living artists, and memorized images from pretrained text-to-image models. I will then discuss our data attribution algorithms for assessing the influence of each training image for a generated sample. Collectively, we aim to enable creators to retain control over the ownership of training images.
Jun-Yan Zhu
Carnegie Mellon University
Jun-Yan Zhu is an Assistant Professor at CMU’s School of Computer Science. Prior to joining CMU, he was a Research Scientist at Adobe Research and a postdoc at MIT CSAIL. He obtained his Ph.D. from UC Berkeley and B.E. from Tsinghua University. He studies computer vision, computer graphics, and computational photography. His current research focuses on generative models for visual storytelling. He is the recipient of the Packard Fellowship, the NSF CAREER Award, the ACM SIGGRAPH Outstanding Doctoral Dissertation Award, and the UC Berkeley EECS David J. Sakrison Memorial Prize for outstanding doctoral research, among other awards.