ANN-topomaps

This repository mainly complements the paper

Valerie Krug, Raihan Kabir Ratul, Christopher Olson and Sebastian Stober.
Visualizing Deep Neural Networks with Topographic Activation Maps. 2023.
In HHAI 2023: Augmenting Human Intellect (pp. 138-152). IOS Press.

available at https://doi.org/10.3233/FAIA230080.

Further, we used this implementation in experiments of the following papers:

Valerie Krug, Christopher Olson and Sebastian Stober.
Relation of Activity and Confidence when Training Deep Neural Networks. 2023.
Uncertainty meets Explainability | Workshop at ECML-PKDD 2023, Torino, Italy

Valerie Krug, Christopher Olson and Sebastian Stober.
Visualizing Bias in Activations of Deep Neural Networks as Topographic Maps. 2023.
Aequitas 2023: Workshop on Fairness and Bias in AI | co-located with ECAI 2023, Kraków, Poland

Usage

This section describes how to compute topographic maps of existing Deep Neural Network models.
Topographic map layouts and visualizations are computed with the run_config.py according to a configuration specified in a config.json.

python3 run_config.py -c </path/to/config.json> -o </output/path/>

In the publication, robustness analyses are performed for multiple runs of the same configuration. These experiments are conducted with the run_experiment.py script, specifying the number of repetitions with the -r argument.

python3 run_experiment.py -c </path/to/config.json> -o </output/path/> -r 100

interactive notebook

For a more interactive use, we provide an ipython notebook in a shared Google Drive folder.
The directory contains a copy of this git repository's source code as well as the pre-trained models used in the publication.
Instead of using configuration files in json format, you can interactively choose and run the configuration with a user interface.

reproducibility

The shared Google Drive folder includes the models used in the publication.
Each experiment can be reproduced using the respective configuration files in configs/paper.

For the racial bias experiment that uses the FairFace data set, you need to first preprocess the data set.
To this end, download the data set from https://github.com/dchen236/FairFace (Data subsection).
Then, use the provided preprocessing script to generate a TF data set:
python3 preprocess_fairface.py -i <path/to/fairface/> -o </output/path/>

Customization

By default, we only allow models that we used in the publication for ease of reproducibility.
adding a pre-trained model:

• add a new model to the models/ directory (saved with tensorflow.keras.Model.save())
  Note: the code expects the naming format <traindata>-<modelname> (e.g. MNIST-MLP)
• add the new data-model combination to allowed_model_values in src.config.check_general_params() adding a data set
• provide a new data loader in src.models.load_data()
• add the new data set name to allowed_dataset_values in src.config.check_general_params()

Visualizing Training Processes

python3 create_checkpoints.py -c "</path/to/config.json>"

sh run_topomapcreator_from_checkpoint.sh