DeepAbstract: Neural Network Abstraction for Accelerating Verification

Pranav Ashok, Vahid Hashemi, Jan Křetínský, Stefanie Mohr

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

27 Scopus citations

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.

Original languageEnglish
Title of host publicationAutomated Technology for Verification and Analysis - 18th International Symposium, ATVA 2020, Proceedings
EditorsDang Van Hung, Oleg Sokolsky
PublisherSpringer Science and Business Media Deutschland GmbH
Pages92-107
Number of pages16
ISBN (Print)9783030591519
DOIs
StatePublished - 2020
Event18th International Symposium on Automated Technology for Verification and Analysis, ATVA 2020 - Hanoi, Viet Nam
Duration: 19 Oct 202023 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12302 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Symposium on Automated Technology for Verification and Analysis, ATVA 2020
Country/TerritoryViet Nam
CityHanoi
Period19/10/2023/10/20

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