Out-of-distribution detection based on subspace projection of high-dimensional features output by the last convolutional layer

This is a PyTorch implementation for detecting out-of-distribution examples in neural networks. The method is described in the paper Out-of-distribution detection based on subspace projection of high-dimensional features output by the last convolutional layer by Qiuyu Zhu,Yiwei He.

Running the code

Dependencies

• CUDA 8.0
• PyTorch
• Anaconda2 or 3

Downloading Out-of-Distribtion Datasets

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

• Tiny-ImageNet (crop)
• Tiny-ImageNet (resize)
• LSUN (crop)
• LSUN (resize)
• iSUN

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

mkdir data
cd data
wget https://www.dropbox.com/s/avgm2u562itwpkl/Imagenet.tar.gz
tar -xvzf Imagenet.tar.gz
cd ..

Running

1. Run train.py to train the ID data classfier.
2. Run OOD_test.py to get the immediate metrics.
3. Run svm.py to fuse the metrics.
4. Run metrics.py to get the experimental results.
5. densnet.py is the DENSNET-100 network, which contains PEDCC layer
6. center_pedcc.py to generate the PEDCC points
7. conf.config is config document.
8. utils.py is training progress code。

License

Please refer to the [LICENSE](ProjOOD/LICENSE at main · Hewell0/ProjOOD (github.com) ).