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3DB: A Framework for Debugging Computer Vision Models
Guillaume Leclerc · Hadi Salman · Andrew Ilyas · Sai Vemprala · Logan Engstrom · Vibhav Vineet · Kai Xiao · Pengchuan Zhang · Shibani Santurkar · Greg Yang · Ashish Kapoor · Aleksander Madry

Tue Nov 29 09:00 AM -- 11:00 AM (PST) @ Hall J #1042

We introduce 3DB: an extendable, unified framework for testing and debugging vision models using photorealistic simulation. We demonstrate, through a wide range of use cases, that 3DB allows users to discover vulnerabilities in computer vision systems and gain insights into how models make decisions. 3DB captures and generalizes many robustness analyses from prior work, and enables one to study their interplay. Finally, we find that the insights generated by the system transfer to the physical world. 3DB will be released as a library alongside a set of examples and documentation. We attach 3DB to the submission.

Author Information

Guillaume Leclerc (Massachusetts Institute of Technology)
Hadi Salman (MIT)
Andrew Ilyas (MIT)
Sai Vemprala (Microsoft)
Logan Engstrom (MIT)
Vibhav Vineet (Microsoft Research)
Kai Xiao (MIT)
Pengchuan Zhang (California Institute of Technology)
Shibani Santurkar (MIT)
Greg Yang (Microsoft Research)
Ashish Kapoor (Microsoft)
Aleksander Madry (MIT)

Aleksander Madry is the NBX Associate Professor of Computer Science in the MIT EECS Department and a principal investigator in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). He received his PhD from MIT in 2011 and, prior to joining the MIT faculty, he spent some time at Microsoft Research New England and on the faculty of EPFL. Aleksander's research interests span algorithms, continuous optimization, science of deep learning and understanding machine learning from a robustness perspective. His work has been recognized with a number of awards, including an NSF CAREER Award, an Alfred P. Sloan Research Fellowship, an ACM Doctoral Dissertation Award Honorable Mention, and 2018 Presburger Award.

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