CLAP Interpretable Predictions 👏🏻

Official codebase for the paper

[1] Provable concept learning for interpretable predictions using variational inference,
Taeb A., Ruggeri N., Schnuck C., Yang F.
(Arxiv preprint)

We present CLAP, an inherently interpretable prediction model.
Its VAE-based architecture allows the discovery and disentanglement of relevant concepts, encoded in the latent space, which are utilized by a simple, concurrently trained classifier.
The final architecture allows to exploit provably interpretable, predictive and minimal concepts to assist practitioners in making informed predictions.

Code usage

To start training CLAP on a dataset:

• download the desired dataset and place it in the ./data directory. Alternatively, change the default data directory specified at src.data.utils.DATA_DIR
• run the terminal command. The datasets available are MPI, Shapes3D, SmallNORB, ChestXRay, PlantVillage [1].

For example, to train CLAP on the MPI dataset, the terminal command is

python main.py --dataset MPI

More options for training, e.g. latent space dimension and regularization parameters, are specified inside main.py.