THE CLEVER HANS EFFECT IN UNSUPERVISED LEARNING

Authors:

Jacob Kauffmann
Jonas Dippel
Lukas Ruff
Wojciech Samek
Gregoire Montavon
Klaus-Robert Müller

Usage:

1. Data Preparation

Download the required datasets:

NIH CXR8: https://academictorrents.com/details/e615d3aebce373f1dc8bd9d11064da55bdadede0
GitHub COVID-19 image collection: https://github.com/ieee8023/covid-chestxray-dataset
ImageNet: https://www.image-net.org/download.php
MVTec-AD: https://www.mvtec.com/company/research/datasets/mvtec-ad

Place the datasets in the data/ directory.

2. Running the experiments

2.1 Software dependencies

All experiments are implemented in Python.
Main dependencies: torch, torchvision, ipython, matplotlib, scikit-learn, scipy, numpy, Pillow, opencv-python-headless.
Experiments provide individual requirements.txt files.

2.2 COVID-19

Navigate to the radiology/ directory.
Open covid19.ipynb with ipython.
Run all cells.
Results can be found in the cell outputs and the results/ sub-directory.

2.3 Anomaly Detection

Additional dependency: Snakemake.
Navigate to the anomalies/ directory.
Run snakemake --cores 12.
Results can be found in the results/ sub-directory.

2.4 Representation Learning

Navigate to the representation/ directory.
Follow instructions in the README.md file in the respective folder.

Contact Information:

Grégoire Montavon: [email protected]
Klaus-Robert Müller: [email protected]