Implementation of the main experiment from https://arxiv.org/abs/2105.14824
Train a model with a bounded logit attention module for the Caltech birds 2011 dataset.
python3 run.py --dataset caltech_birds2011 --preset BLA
Weights of trained models are saved in weights_dir/
and logs of loss, accuracy, and top-5 accuracy for the uninterpretable baseline model, the model using the soft training-time explanations, and
the model using the hard test-time explanations are saved in logs/.
-d,--dataset
Name of the dataset to use, options are {cats_vs_dogs, stanford_dogs, caltech_birds2011}
-s,--preset
Preset configurations (see paper for details) {L2X-F , BLA, BLA-T, BLA-PH}
L2X-F: learning to explain on feature level (fixed size explanations)BLA: bounded logit attention (without thresholding)BLA-T: bounded logit attention with thresoldingBLA-PH: post-hoc setup (head is frozen and only the explanation module is trained)
(Overwritten by presets where applicable.)
-f, --fixed-size
Produce fixed size explanations (i.e. learning to explain on feature level, not bounded logit attention)
-t, --thresholding
Thresholding using gamma = 0.02.
-p, --post-hoc
Post-hoc setup, i.e. freeze head during training of explanation module
-r, --force-retraining
Retrain, rather than using saved weights from weights_dir/