XTxiatong / Hybrid-EDL

Hybrid-EDL

This repository includes the code to reproduce our experiments in the paper entitled

Hybrid-EDL: Improving Evidential Deep Learning for Uncertainty Quantification on Imbalanced Data

presented in 2022 Trustworthy and Socially Responsible Machine Learning (TSRML 2022) co-located with NeurIPS 2022

Requirements

To install requirements:

conda env create -f environment.yaml
conda activate hybrid-edl
conda env list

Training & Evaluation

We use the experiments on CIFAR10 as the example to show how to run our codes. To train the model(s) in the paper, run

sh run.sh

This is equivalent to

python run.py --name cifar10_vgg_HeavyImbalance --seed 100 

Trained Models

Trained models are saved in the folder saved_models.

Results

Model outputs are saved in the folder saved_results/cifar10_vgg_HeavyImbalance. Numerical results in Table 1 can be re-produced by python calibration_imbanlance.py. The exampled output present the results when training the model with heavily imbalanced:

Before calibration:
acc: 0.6865
UAR: 0.6865
UAR majority 0.985
UAR minority: 0.6533333333333333
ECE: 0.17766500000000002
OOD ROAUC: 0.6816205016902275

After calibration:
acc: 0.7217
UAR: 0.7217
UAR majority 0.984
UAR minority: 0.6925555555555556
ECE: 0.16402499999999998
OOD ROAUC: 0.7010376805470191

*Note: the folder src is adatped from repo https://github.com/sharpenb/Posterior-Network.git

This is the vanilla EDL implementation we compared with.