Explanation_by_Progressive_Exaggeration

Official Tensorflow implementation of ICLR 2020 paper: Explanation By Progressive Exaggeration.

Experimental Results

Qualitative Results

Age-Attribute Experiment

Training graphs and qualitative results

./notebooks/Process_Classifier_Output.ipynb

Installation

$ pip install -r requirements.txt

Usage

Download the CelebA dataset and create train and test fold for the classifier.

CelebA dataset is downloaded and saved at ./data/CelebA. IPython notebook creates text files with file names and labels and save them at ./data/CelebA/. These text files are used as input data to train the classifier.

./notebooks/Data_Processing.ipynb

Train a classifier. Skip this step if you have a pretrained classifier. The output of the classifier is saved at: $log_dir$/$name$.

2.a. To train a multi-label classifier on all 40 attributes

python train_classifier.py --config 'configs/celebA_DenseNet_Classifier.yaml'

2.b. To train a binary classifier on 1 attribute

python train_classifier.py --config 'configs/celebA_Smile_DenseNet_Classifier.yaml'

Process the output of the classifier and create input for Explanation model by discretizing the posterior probability. The input data for the Explanation model is saved at: $log_dir$/$name$/explainer_input/
```
./notebooks/Process_Classifier_Output.ipynb
```

Trainer explainer model. The output is saved at: $log_dir$/$name$.

python train_explainer.py --config 'configs/celebA_Young_Explainer.yaml'

Explore the trained Explanation model and see qualitative results.
```
./notebooks/Test_Explainer.ipynb
```
Save results of the trained Explanation model for quantitative experiments.
```
python test_explainer.py --config 'configs/celebA_Young_Explainer.yaml'
```
Use the saved results to perform experiments as shown in paper
```
./notebooks/Experiment_CelebA.ipynb 
```
Cite

@inproceedings{
Singla2020Explanation,
title={Explanation  by Progressive  Exaggeration},
author={Sumedha Singla and Brian Pollack and Junxiang Chen and Kayhan Batmanghelich},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=H1xFWgrFPS}
}

batmanlab / Explanation_by_Progressive_Exaggeration

readme