research-article

Dynamic slicing for deep neural networks

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Yunxin LiuAuthors Info & Claims

ESEC/FSE 2020: Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering

Pages 838 - 850

https://doi.org/10.1145/3368089.3409676

Published: 08 November 2020 Publication History

Abstract

Program slicing has been widely applied in a variety of software engineering tasks. However, existing program slicing techniques only deal with traditional programs that are constructed with instructions and variables, rather than neural networks that are composed of neurons and synapses. In this paper, we introduce NNSlicer, the first approach for slicing deep neural networks based on data-flow analysis. Our method understands the reaction of each neuron to an input based on the difference between its behavior activated by the input and the average behavior over the whole dataset. Then we quantify the neuron contributions to the slicing criterion by recursively backtracking from the output neurons, and calculate the slice as the neurons and the synapses with larger contributions. We demonstrate the usefulness and effectiveness of NNSlicer with three applications, including adversarial input detection, model pruning, and selective model protection. In all applications, NNSlicer significantly outperforms other baselines that do not rely on data flow analysis.

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Cited By

Bi XZhao RQi BSun HGao XYu YLiang Xd'Amorim M(2024)ModelFoundry: A Tool for DNN Modularization and On-Demand Model Reuse Inspired by the Wisdom of Software EngineeringCompanion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering10.1145/3663529.3663810(617-621)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3663529.3663810
Ghanbari AChristakis MPradel M(2024)Decomposition of Deep Neural Networks into Modules via Mutation AnalysisProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3680390(1669-1681)Online publication date: 11-Sep-2024
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Jiang JYang JZhang YWang ZYou HChen J(2024)A Post-training Framework for Improving the Performance of Deep Learning Models via Model TransformationACM Transactions on Software Engineering and Methodology10.1145/363001133:3(1-41)Online publication date: 15-Mar-2024
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Index Terms

Dynamic slicing for deep neural networks
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Software and its engineering
  1. Software organization and properties
    1. Software functional properties
      1. Formal methods
        Dynamic analysis

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cover image ACM Conferences

ESEC/FSE 2020: Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering

November 2020

1703 pages

ISBN:9781450370431

DOI:10.1145/3368089

General Chair:
Prem Devanbu
University of California at Davis, USA
,
Program Chairs:
Myra Cohen
Iowa State University, USA
,
Thomas Zimmermann
Microsoft Research, USA

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ESEC/FSE '20: 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

November 8 - 13, 2020

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Bi XZhao RQi BSun HGao XYu YLiang Xd'Amorim M(2024)ModelFoundry: A Tool for DNN Modularization and On-Demand Model Reuse Inspired by the Wisdom of Software EngineeringCompanion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering10.1145/3663529.3663810(617-621)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3663529.3663810
Ghanbari AChristakis MPradel M(2024)Decomposition of Deep Neural Networks into Modules via Mutation AnalysisProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3680390(1669-1681)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3650212.3680390
Jiang JYang JZhang YWang ZYou HChen J(2024)A Post-training Framework for Improving the Performance of Deep Learning Models via Model TransformationACM Transactions on Software Engineering and Methodology10.1145/363001133:3(1-41)Online publication date: 15-Mar-2024
https://dl.acm.org/doi/10.1145/3630011
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