David Forsyth
Talk: What diffusion models do: not what it says on the box, and worth fixing
Diffusion models generate images from noise, and are now the backbone of image generation activities. I show that: generative models "know" properties of scenes that aren't in their training data; don't "know" other properties of the natural world; and leave distinctive fingerprints in the images they generate. This doesn't matter if the application of the models is to make commercial art cheaper or public discourse nastier, but matters a lot if the models are used to solve, say, inverse problems.
BIO:
I am currently Fulton-Watson-Copp chair in computer science at U. Illinois at Urbana-Champaign, where I moved from U.C Berkeley, where I was also full professor. I have occupied the Fulton-Watson-Copp chair in Computer Science at the University of Illinois since 2014. I have published over 200 papers on computer vision, computer graphics and machine learning. I have served as program co-chair for IEEE Computer Vision and Pattern Recognition in 2000, 2011, 2018 and 2021, general co-chair for CVPR 2006 and 2015 and ICCV 2019, program co-chair for the European Conference on Computer Vision 2008, Senior Advisor to the Program Chairs for CVPR 2024, 2025, 2026 and ICCV 2025, and am a regular member of the program committee of all major international conferences on computer vision. I have served six years on the SIGGRAPH program committee, and am a regular reviewer for that conference. I have received best paper awards at the International Conference on Computer Vision and at the European Conference on Computer Vision. I received an IEEE technical achievement award for 2005 for my research. I became an IEEE Fellow in 2009, and an ACM Fellow in 2014. I received the Mark Everingham Prize in 2024. My textbook, "Computer Vision: A Modern Approach" (joint with J. Ponce and published by Prentice Hall) is now widely adopted as a course text (adoptions include MIT, U. Wisconsin-Madison, UIUC, Georgia Tech and U.C. Berkeley). A further textbook, “Probability and Statistics for Computer Science”, came out some years ago; yet another (“Applied Machine Learning”) has appeared recently. I have served two terms as Editor in Chief, IEEE TPAMI. I serve on a number of scientific advisory boards, and have an active practice as an expert witness.