Skip to content

Latest commit

 

History

History
66 lines (50 loc) · 2.37 KB

README.md

File metadata and controls

66 lines (50 loc) · 2.37 KB
Raw

VeriX: Towards Verified Explainability of Deep Neural Networks

Min Wu, Haoze Wu, Clark Barrett.

The accompanying paper VeriX: towards Verified eXplainability of deep neural networks is accepted by NeurIPS 2023.

Citation

@inproceedings{VeriX,
 author = {Wu, Min and Wu, Haoze and Barrett, Clark},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine},
 pages = {22247--22268},
 publisher = {Curran Associates, Inc.},
 title = {VeriX: Towards Verified Explainability of Deep Neural Networks},
 url = {https://proceedings.neurips.cc/paper_files/paper/2023/file/46907c2ff9fafd618095161d76461842-Paper-Conference.pdf},
 volume = {36},
 year = {2023}
}

Example Usage

For the MNIST dataset, to compute the VeriX explanation for the 10th image in the test set x_test and the neural network mnist-10x2.onnx in folder models/.

verix = VeriX(dataset="MNIST",
              image=x_test[10],
              model_path="models/mnist-10x2.onnx")
verix.traversal_order(traverse="heuristic")
verix.get_explanation(epsilon=0.05)

Use the heuristic feature-level sensitivity method to set the traversal order, and then set the perturbation magnitude epsilon to obtain the explanation. Be default, the original image, the sensitivity, and the explanation will be plotted and saved.

See mnist.py for a full example usage. The GTSRB dataset is also supported as in gtsrb.py.

To use VeriX, a neural network verification tool called Marabou and an LP solver called Gurobi need to be installed in advance.

git clone https://github.com/NeuralNetworkVerification/Marabou.git
cd path/to/marabou/repo/folder
mkdir build 
cd build
cmake .. -DENABLE_GUROBI=ON -DBUILD_PYTHON=ON
cmake --build . -j 12

More details on how to install Marabou with Gurobi enabled can be found here.

Developer's Platform

This is for reference only - feel free to set up your own environment.

python 		3.7.13
keras		2.9.0
tensorflow 	2.9.1
onnx 		1.10.2
onnxruntime 	1.10.0
tf2onnx 	1.9.3

Remark

Thanks a lot for your interest in our work. Any questions please feel free to contact us: minwu@cs.stanford.edu.