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

Keywords: robustness, debugging, simulation, computer vision, deep learning

TL;DR: We introduce 3DB: an extendable, unified framework for testing and debugging vision models using photorealistic simulation.

Abstract: 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.

Community Implementations: [ 4 code implementations](https://www.catalyzex.com/paper/arxiv:2106.03805/code)