Use Perturbations when Learning from Explanations

Code accompanying submission titled "Use Perturbations when Learning from Explanations" to NeuriIPS 2023.

Algorithm implementations can be found in src/module and dataset implementations in src/datasets.
Hyperparameter searches and best hyperoparameters are documented in src/configs/expts.py.

How to run the code

Dependencies

• Python and Conda

• Setup the conda environment IBPEx by running:

  conda env create -f setup_dependencies.yml

  If you want to make use of your GPU, you might have to install a cuda-enabled pytorch version manually. Use the appropriate command provided here to achieve this. Before running code, activate the environment IBPEx.

  source activate IBPEx

Datasets

• We have one synthetic dataset (decoy-MNIST) and two real dataset (ISIC and Plant phenotyping).
• The default experiment settings for each dataset are in src/configs/dataset_defaults.py

Algorithms

• We have 5 methods (RRR, IBP-Ex, PGD-Ex, CDEP, CoRM) in src/module, whose parent module is base_trainer.py.
• The default experiment settings for each method are in src/configs/alg_defaults.py

Experiments

• Run the code with the best hyperparameter setting we found for each dataset in src/configs/expts.py.

• For example, to train the model using our IBP-Ex method on the Plant phenotyping dataset,

  python main.py --name 'ibp_plant_expt3'

• View all possible command-line options by running

  python main.py --help