Principled Detection of Out-of-Distribution Examples in Neural Networks
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code Create Jun 14, 2017
figures Add files via upload Jun 5, 2017 Update Jun 28, 2017

ODIN: Out-of-Distribution Detector for Neural Networks

This is a PyTorch implementation for detecting out-of-distribution examples in neural networks. The method is described in the paper Principled Detection of Out-of-Distribution Examples in Neural Networks by S. Liang, Yixuan Li and R. Srikant. The method reduces the false positive rate from the baseline 34.7% to 4.3% on the DenseNet (applied to CIFAR-10) when the true positive rate is 95%.

Experimental Results

We used two neural network architectures, DenseNet-BC and Wide ResNet. The PyTorch implementation of DenseNet-BC is provided by Andreas Veit and Brandon Amos. The PyTorch implementation of Wide ResNet is provided by Sergey Zagoruyko. The experimental results are shown as follows. The definition of each metric can be found in the paper. performance

Pre-trained Models

We provide four pre-trained neural networks: (1) two DenseNet-BC networks trained on CIFAR-10 and CIFAR-100 respectively; (2) two Wide ResNet networks trained on CIFAR-10 and CIFAR-100 respectively. The test error rates are given by:

Architecture CIFAR-10 CIFAR-100
DenseNet-BC 4.81 22.37
Wide ResNet 3.71 19.86

Running the code


  • CUDA 8.0

  • PyTorch

  • Anaconda2 or 3

  • At least three GPU

    Note: Reproducing results of DenseNet-BC only requires one GPU, but reproducing results of Wide ResNet requires three GPUs. Single GPU version for Wide ResNet will be released soon in the future.

Downloading Out-of-Distribtion Datasets

We provide download links of five out-of-distributin datasets:

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

mkdir data
cd data
tar -xvzf Imagenet.tar.gz
cd ..

Downloading Neural Network Models

We provide download links of four pre-trained models.

Here is an example code of downloading DenseNet-BC trained on CIFAR-10. In the root directory, run

mkdir models
cd models
tar -xvzf densenet10.pth.tar.gz
cd ..


Here is an example code reproducing the results of DenseNet-BC trained on CIFAR-10 where TinyImageNet (crop) is the out-of-distribution dataset. The temperature is set as 1000, and perturbation magnitude is set as 0.0014. In the root directory, run

cd code
# model: DenseNet-BC, in-distribution: CIFAR-10, out-distribution: TinyImageNet (crop)
# magnitude: 0.0014, temperature 1000, gpu: 0
python --nn densenet10 --out_dataset Imagenet --magnitude 0.0014 --temperature 1000 --gpu 0

Note: Please choose arguments according to the following.


  • args.nn: the arguments of neural networks are shown as follows

    Nerual Network Models args.nn
    DenseNet-BC trained on CIFAR-10 densenet10
    DenseNet-BC trained on CIFAR-100 densenet100
  • args.out_dataset: the arguments of out-of-distribution datasets are shown as follows

    Out-of-Distribution Datasets args.out_dataset
    Tiny-ImageNet (crop) Imagenet
    Tiny-ImageNet (resize) Imagenet_resize
    LSUN (crop) LSUN
    LSUN (resize) LSUN_resize
    iSUN iSUN
    Uniform random noise Uniform
    Gaussian random noise Gaussian
  • args.magnitude: the optimal noise magnitude can be found below. In practice, the optimal choices of noise magnitude are model-specific and need to be tuned accordingly.

    Out-of-Distribution Datasets densenet10 densenet100 wideresnet10 wideresnet100
    Tiny-ImageNet (crop) 0.0014 0.0014 0.0005 0.0028
    Tiny-ImageNet (resize) 0.0014 0.0028 0.0011 0.0028
    LSUN (crop) 0 0.0028 0 0.0048
    LSUN (resize) 0.0014 0.0028 0.0006 0.002
    iSUN 0.0014 0.0028 0.0008 0.0028
    Uniform random noise 0.0014 0.0028 0.0014 0.0028
    Gaussian random noise 0.0014 0.0028 0.0014 0.0028
  • args.temperature: temperature is set to 1000 in all cases.

  • args.gpu: make sure you use the following gpu when running the code:

    Neural Network Models args.gpu
    densenet10 0
    densenet100 0
    wideresnet10 1
    wideresnet100 2


Here is an example of output.

Neural network architecture:          DenseNet-BC-100
In-distribution dataset:                     CIFAR-10
Out-of-distribution dataset:     Tiny-ImageNet (crop)

                          Baseline         Our Method
FPR at TPR 95%:              34.8%               4.3% 
Detection error:              9.9%               4.6%
AUROC:                       95.3%              99.1%
AUPR In:                     96.4%              99.2%
AUPR Out:                    93.8%              99.1%