neurips2020

Deep Evidential Regression

Alexander Amini, Wilko Schwarting, Ava Soleimany, Daniela Rus. NeurIPS 2020

This repository contains the code to reproduce all results presented in the NeurIPS submission: "Deep Evidential Regression".

@article{amini2020deep,
  title={Deep evidential regression},
  author={Amini, Alexander and Schwarting, Wilko and Soleimany, Ava and Rus, Daniela},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  year={2020}
}

Setup

Download datasets

To get started, first download the relevant datasets and some pre-trained models (if you don't want to re-train from scratch). These datasets include:

1. NYU Depth v2 dataset
2. Apolloscapes depth
3. UCI regression tasks (already pre-downloaded in ./data)

To download run the following commands from a Unix shell:

cd evidential_deep_learning/neurips2020
bash ./download_data.sh

We also include pre-trained models, in case reviewers would like to use these to produce results without retraining from scratch. If you would like to re-train, we provide the code to do so - we include pre-trained models here only for added convienence.

Software environment

We package our codebase into a conda environment, with all dependencies listed in environment.yml. To create a local copy of this environment and activate it, run the following commands:

conda env create -f environment.yml
conda activate evidence

Reproducing Results

Monocular Depth

The easiest way to reproduce the depth results presented in the submission would be to run:

python gen_depth_results.py

This command will automatically use the pre-trained models downloaded above, if you would like to retrain the depth models from scratch you can run:

python train_depth.py  [--model {evidential, dropout, ensemble}]

Note that the path to any new trained models should be replaced in the trained_models parameter within gen_depth_results.py if you'd like the plots to reflect changes in this newly trained model.

UCI and Cubic Examples

Results for the cubic example figures can be reproduced by running:

python run_cubic_tests.py

Results for the UCI benchmarking tasks can be reproduced by running:

python run_uci_dataset_tests.py [-h] [--num-trials NUM_TRIALS]
                                [--num-epochs NUM_EPOCHS]
                                [--datasets {yacht, ...}]]

Enjoy!