@inproceedings{47a5a1de7eb74f8486231143eaddad2b,
title = "DeepAbstract: Neural Network Abstraction for Accelerating Verification",
abstract = "While abstraction is a classic tool of verification to scale it up, it is not used very often for verifying neural networks. However, it can help with the still open task of scaling existing algorithms to state-of-the-art network architectures. We introduce an abstraction framework applicable to fully-connected feed-forward neural networks based on clustering of neurons that behave similarly on some inputs. For the particular case of ReLU, we additionally provide error bounds incurred by the abstraction. We show how the abstraction reduces the size of the network, while preserving its accuracy, and how verification results on the abstract network can be transferred back to the original network.",
author = "Pranav Ashok and Vahid Hashemi and Jan K{\v r}et{\'i}nsk{\'y} and Stefanie Mohr",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 18th International Symposium on Automated Technology for Verification and Analysis, ATVA 2020 ; Conference date: 19-10-2020 Through 23-10-2020",
year = "2020",
doi = "10.1007/978-3-030-59152-6_5",
language = "English",
isbn = "9783030591519",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "92--107",
editor = "Hung, {Dang Van} and Oleg Sokolsky",
booktitle = "Automated Technology for Verification and Analysis - 18th International Symposium, ATVA 2020, Proceedings",
}