/DDeXU

Explainable Uncertainty From Latent Space: Leveraging Probabilistic Circuits for Robust Deep Learning

Primary LanguageJupyter Notebook

DDeXU - Deep Deterministic and Explainable Uncertainty

This repo contains the code to my masterthesis at TU Darmstadt (Quantifying and Explaining Latent Uncertainty: Probabilistic Circuits for Robust Deep Learning).

As backbone, efficientnet-v2 is used, while the probabilistic circuit employed is simple-einet.

Please note that the code in this repo is research-code and not very usable at the moment without modifications.

Dependencies

The dependencies can be found in the Dockerfile.

Project structure

All models can be found in Models.py. This includes DDeXU (called 'EfficientNetSPN' there) and the baseline methods that we compare to.

To run an experiment load the corresponding run-method from the experiment, e.g. start_cifar10_calib_run(...) from experiments/cifar10_calib_experiment.py, or modify experiment_runner.py.