Visual Contrastive Explanation

This repository contains the code for the paper "VAE-CE: Visual Contrastive Explanation using Disentangled VAEs" [IDA 2022], [arxiv].

Installation

Required packages are listed in requirements.txt. The code was developed/tested using Python 3.7.3.

Training and testing

To train and evaluate methods you can use the supplied training scripts:

python train.py --type cd --epoch 50 --save True --save_name cd
python train.py --type vaece --cd_name cd --epoch 20 --save True --save_name vaece
python evaluate.py --metric_name metric --type vaece --load_name vaece --cd_name cd

Data

Data can be generated by running the corresponding scripts in data/:

python -m data.synthetic.generate
python -m data.mnist.generate

The synthetic (training and testing) data as used in the paper can be downloaded from here.

Implementations

The implementations of all models are provided in model/: Shared components in component.py, with the training procedures in r_model.py and cd_model.py.

Explanation generation and the explanation alignment cost (eac) computation are provided in explanation/: explanation.py and evaluation.py.

The evaluation of all metrics is provided by MetricComputation in testing/metric.py.

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If you found our work useful in your research, please consider citing:

@inproceedings{poels2022vaece,
  title={{VAE-CE}: Visual Contrastive Explanation Using Disentangled {VAEs}},
  author={Poels, Yoeri and Menkovski, Vlado},
  booktitle={Advances in Intelligent Data Analysis XX},
  pages={237--250},
  year={2022}
}