Need help with energy_ood?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

wetliu
190 Stars 31 Forks Apache License 2.0 21 Commits 0 Opened issues

Services available

!
?

Need anything else?

Contributors list

# 344,683
Shell
C
C++
cuda
16 commits

Energy-based Out-of-distribution Detection (Energy OOD)

This repository is the official implementation of Energy-based Out-of-distribution Detection by Weitang Liu, Xiaoyun Wang, John Owens and Yixuan Li. This method is an effective and easy OOD detector with and without fine-tuning. Our code is implemented with courtesy of Outlier-Exposure. If you have any code related questions, such as this issue and this issue, we highly recommened to check the couterpart in Outlier-Exposure.

image

Pretrained Models and Datasets

Pretrained models are provided in folder

./CIFAR/snapshots/

Please download the datasets in folder

./data/

Testing and Fine-tuning

run energy score testing for cifar10 WRN

test
bash run.sh energy 0

run energy score testing for cifar100 WRN

test
bash run.sh energy 1

run energy score training and testing for cifar10 WRN

train
bash run.sh energy_ft 0

run energy score training and testing for cifar100 WRN

train
bash run.sh energy_ft 1

Results

Our model achieves the following average performance on 6 OOD datasets:

1. MSP vs energy score with and without fine-tuned on CIFAR-10

| Model name | FPR95 | | ------------------ |---------------- | | Softmax score | 51.04% | | Energy score (ours) | 33.01% | | Softmax score with fine-tune | 8.53% | | Energy score with fine-tune (ours) | 3.32% |

2. CIFAR-10 (in-distribution) vs SVHN (out-of-distribution) Score Distributions

image

3. Performance among different baselines for WideResNet

CIFAR-10: | Model name | FPR95 | | ------------------ |---------------- | | Softmax score | 51.04% | | Energy score (ours) | 33.01% | | ODIN | 35.71% | | Mahalanobis | 37.08% | | Outlier Exposure| 8.53% | | Energy score with fine-tune (ours) | 3.32% |

CIFAR-100: | Model name | FPR95 | | ------------------ |---------------- | | Softmax score | 80.41% | | Energy score (ours) | 73.60% | | ODIN | 74.64% | | Mahalanobis | 54.64% | | Outlier Exposure| 58.10% | | Energy score with fine-tune (ours) | 47.55% |

Outlier Datasets

These experiments make use of numerous outlier datasets. Links for less common datasets are as follows, 80 Million Tiny Images Textures, Places365, LSUN-C, LSUN-R, iSUN and SVHN.

Citation

 @article{liu2020energy,
      title={Energy-based Out-of-distribution Detection},
      author={Liu, Weitang and Wang, Xiaoyun and Owens, John and Li, Yixuan},
      journal={Advances in Neural Information Processing Systems},
      year={2020}
 } 

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.