SyReNN: A Tool for Analyzing Deep Neural Networks

Matthew Sotoudeh, Zhe Tao and Thakur, Aditya V.
International Journal on Software Tools for Technology Transfer (STTT), 2023

Deep Neural Networks (DNNs) are rapidly gaining popularity in a variety of important domains. Unfortunately, modern DNNs have been shown to be vulnerable to a variety of attacks and buggy behavior. This has motivated recent work in formally analyzing the properties of such DNNs. This paper introduces SyReNN, a tool for understanding and analyzing a DNN by computing its symbolic representation. The key insight is to decompose the DNN into linear functions. Our tool is designed for analyses using low-dimensional subsets of the input space, a unique design point in the space of DNN analysis tools. We describe the tool and the underlying theory, then evaluate its use and performance on three case studies: computing Integrated Gradients, visualizing a DNN’s decision boundaries, and repairing buggy DNNs.

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@article{STTT2023,
  author = {Matthew Sotoudeh, Zhe Tao and Thakur, Aditya V.},
  title = {SyReNN: A Tool for Analyzing Deep Neural Networks},
  booktitle = {International Journal on Software Tools for Technology Transfer {(STTT)}},
  publisher = {Springer},
  year = {2023},
  doi = {10.1007/s10009-023-00695-1}
}