Install TF-DM and run the setup.sh script once.
./perform_fi_exp.py -b $bmark -m $model
where $bmark is the benchmark name and $model is the model name.
Example with running experiments on CIFAR-10, on the VGG16 model, injecting 10% mislabelling errors into the training data.
cp ./confFiles/label_err-10.yaml ./confFiles/sample.yaml
./perform_fi_exp.py -b cifar10 -m VGG16
Please cite the following paper if you find NN-ensemble useful
Understanding the Resilience of Neural Network Ensembles against Faulty Training Data, Abraham Chan, Niranjhana Narayananan, Arpan Gujarati, Karthik Pattabiraman, and Sathish Gopalakrishnan, Proceedings of the IEEE International Symposium on Quality, Reliability and Security (QRS), 2021.