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IGEOOD: An Information Geometry Approach to Out-of-Distribution Detection

This repository is the official implementation of IGEOOD: An Information Geometry Approach to Out-of-Distribution Detection.

Figure 1. Probability distributions of the IGEOOD score under three different settings for a pre-trained DenseNet on CIFAR-10 for in-distribution and OOD data (Tiny-ImageNet downsampled).

Installation instructions

To install requirements, run:

pip install -r requirements.txt

(Optional) With Virtual environment

python3 -m venv env
source env/bin/activate
pip install -r requirements.txt

Preparation

Download out-of-distribution datasets:

Out-of-distribution datasets

• Tiny-ImageNet (resize)
• LSUN (resize)
• iSUN
• Textures
• Places365
• Chars

Here is an example code of downloading Tiny-ImageNet dataset. In the root directory, run

cd datasets
wget https://www.dropbox.com/s/kp3my3412u5k9rl/Imagenet_resize.tar.gz
tar -xvzf Imagenet_resize.tar.gz
cd ..

Alternatively, run the script download.sh to download all datasets to the datasets/ folder.

bash download.sh

In-distribution datasets

The in-distribution datasets CIFAR-10, CIFAR-100 and SVHN, are directly downloaded from PyTorch's official mirrors when called in code, i.e., download flag is set to True when loading these datasets.

Pre-trained Models

The pre-trained models for reproducing results is available for download in the script download.sh. They will be downloaded to the folder pre_trained/.

Evaluation

We provide a simple cli tool via the file eval.py to reproduce the main results of this paper.

For example, to run IGEOOD OOD detection method in a Black-Box setting on DenseNet trained on CIFAR-10 with Tiny-ImageNet as out-of-distribution dataset, run:

python eval.py igeoodlogits --nn densenet --in-dataset cifar10 --out-dataset Imagenet_resize --temperature 5.3 --epsilon 0 

Refer to the documentation below for the cli tool.

python eval.py -h
usage: eval.py [-h] [-nn {densenet,resnet,densenet2}]
               [-i {CIFAR10,cifar10,CIFAR100,cifar100,SVHN,svhn}]
               [-o {Imagenet_resize,LSUN_resize,CIFAR10,CIFAR100,SVHN,iSUN,ADVdensenet10,ADVdensenet100,ADVdensenet_svhn,ADVresnet_cifar10,ADVresnet_cifar100,ADVresnet_svhn,Places365,Textures,Chars74K,gaussian_noise_dataset}]
               [-eps EPSILON] [-EPSS EPSILONS [EPSILONS ...]]
               [-T TEMPERATURE] [-TS TS [TS ...]] [-r REWRITE]
               [-b BATCH_SIZE] [-gpu GPU]
               {igeood_logits,igeoodlogits,igeood_plus,igeood_adv,igeood,igeood_adv_plus,msp,odin,energy,mahalanobis,mahalanobis_adv}

Reproduce results from IGEOOD: Information Geometry Approach to Out-
of-Distribution Detection paper.
...
example: python eval.py igeoodlogits -nn densenet10 -o Imagenet_resize
-eps 0.0012 -T 5.3 -gpu 0

The core implementation files of the OOD detection methods is in the folder src/.

Citation

Bibtex:

@inproceedings{
anonymous2022igeood,
title={Igeood: An Information Geometry Approach to Out-of-Distribution Detection},
author={Anonymous},
booktitle={Submitted to The Tenth International Conference on Learning Representations },
year={2022},
url={https://openreview.net/forum?id=mfwdY3U_9ea},
note={under review}
}