research-article

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Provable repair of deep neural networks

Authors:

Matthew Sotoudeh,

Aditya V. ThakurAuthors Info & Claims

PLDI 2021: Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation

Pages 588 - 603

https://doi.org/10.1145/3453483.3454064

Published: 18 June 2021 Publication History

Abstract

Deep Neural Networks (DNNs) have grown in popularity over the past decade and are now being used in safety-critical domains such as aircraft collision avoidance. This has motivated a large number of techniques for finding unsafe behavior in DNNs. In contrast, this paper tackles the problem of correcting a DNN once unsafe behavior is found. We introduce the provable repair problem, which is the problem of repairing a network N to construct a new network N′ that satisfies a given specification. If the safety specification is over a finite set of points, our Provable Point Repair algorithm can find a provably minimal repair satisfying the specification, regardless of the activation functions used. For safety specifications addressing convex polytopes containing infinitely many points, our Provable Polytope Repair algorithm can find a provably minimal repair satisfying the specification for DNNs using piecewise-linear activation functions. The key insight behind both of these algorithms is the introduction of a Decoupled DNN architecture, which allows us to reduce provable repair to a linear programming problem. Our experimental results demonstrate the efficiency and effectiveness of our Provable Repair algorithms on a variety of challenging tasks.

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Liang ZWu TZhao CLiu WXue BYang WWang JHuang W(2025)BIRDNN: Behavior-Imitation Based Repair for Deep Neural NetworksNeural Networks10.1016/j.neunet.2024.106949183(106949)Online publication date: Mar-2025
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Provable repair of deep neural networks

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cover image ACM Conferences

PLDI 2021: Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation

June 2021

1341 pages

ISBN:9781450383912

DOI:10.1145/3453483

General Chair:
Stephen N. Freund
Williams College, USA
,
Program Chair:
Eran Yahav
Technion, Israel

Copyright © 2021 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 18 June 2021

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PLDI '21: 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation

June 20 - 25, 2021

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Liang ZWu TZhao CLiu WXue BYang WWang JHuang W(2025)BIRDNN: Behavior-Imitation Based Repair for Deep Neural NetworksNeural Networks10.1016/j.neunet.2024.106949183(106949)Online publication date: Mar-2025
https://doi.org/10.1016/j.neunet.2024.106949
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https://dl.acm.org/doi/10.1007/s11704-024-3902-x
Zhong SLe DLiu ZJiang ZYe AZhang JYuan JZhou KXu ZMa JXu SChaudhary VHu XSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)GNNs also deserve editing, and they need it more than onceProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694623(61727-61746)Online publication date: 21-Jul-2024
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Xing YGuo QCao XTsang IMa LCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)MetaRepair: Learning to Repair Deep Neural Networks from Repairing ExperiencesProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680638(1781-1790)Online publication date: 28-Oct-2024
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