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NeurIPS2022Top-tier venue

3DB: A Framework for Debugging Computer Vision Models

Guillaume Leclerc, Hadi Salman, Andrew Ilyas, Sai Vemprala, Logan Engstrom, Vibhav Vineet, Kai Yuanqing Xiao, Pengchuan Zhang, Shibani Santurkar, Greg Yang, Ashish Kapoor, Aleksander Madry

2022Year
45Citations
15Top-tier citations

Abstract

We introduce 3DB: an extendable, uni ed 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 nd that the insights generated by the system transfer to the physical world. We are releasing 3DB as a library 1 alongside a set of example analyses 2 , guides 3 , and documentation 4 . * Work partially completed while at Microsoft Research. † Equal contribution.

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