This repository contains the code for DeDUCE: Generating Counterfactual Explanations Efficiently by Benedikt Höltgen, Lisa Schut, Jan M. Brauner, Yarin Gal.
run_training.py
trains ResNets, drawing on files in DDU
.
Sanity checks for the DDU models are performed in nb_DDU_FashionMNIST.ipynb
.
run_DeDUCE.py
, run_JSMA.py
, and run_REVISE.py
generate counterfactuals, drawing on files in CE
.
nb_AnoGAN_eval.ipynb
is used for tuning the AnoGAN metric as well as computing scores, drawing on files in metrics
.
Sanity checks are performed in nb_metrics_EMNIST.ipynb
.
nb_tune_DeDUCE.ipynb
and nb_tune_REVISE.ipynb
are used for tuning the respective algorithm on the validation set examples in valset_batch
.
nb_eval_testset.ipynb
performs the testset evaluation, with files in _testset_results
.
nb_eval_testset2-5.ipynb
performs further evaluations, on additional testsets, with files in _testset_results*
.