3DB: A Framework for Debugging Computer Vision Models

Autor:	Leclerc, Guillaume, Salman, Hadi, Ilyas, Andrew, Vemprala, Sai, Engstrom, Logan, Vineet, Vibhav, Xiao, Kai, Zhang, Pengchuan, Santurkar, Shibani, Yang, Greg, Kapoor, Ashish, Madry, Aleksander
Rok vydání:	2021
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. We are releasing 3DB as a library (https://github.com/3db/3db) alongside a set of example analyses, guides, and documentation: https://3db.github.io/3db/ .
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2106.03805 Zobrazit plný text záznamu View this record from Arxiv