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Adversarial Reciprocal Points Learning for Open Set Recognition

Official PyTorch implementation of "Adversarial Reciprocal Points Learning for Open Set Recognition".

1. Requirements

Environments

Currently, requires following packages

  • python 3.6+
  • torch 1.4+
  • torchvision 0.5+
  • CUDA 10.1+
  • scikit-learn 0.22+

Datasets

For Tiny-ImageNet, please download the following datasets to ./data/tiny_imagenet.

2. Training & Evaluation

Open Set Recognition

To train open set recognition models in paper, run this command:

python osr.py --dataset <DATASET> --loss <LOSS>

Option --loss can be one of ARPLoss/RPLoss/GCPLoss/Softmax. --dataset is one of mnist/svhn/cifar10/cifar100/tiny_imagenet. To run ARPL+CS, add --cs after this command.

Out-of-Distribution Detection

To train out-of-distribution models in paper, run this command:

python ood.py --dataset <DATASET> --out-dataset <DATASET> --model <NETWORK> --loss <LOSS>

Option --out-dataset denotes the out-of-distribution dataset for evaluation. --loss can be one of ARPLoss/RPLoss/GCPLoss/Softmax. --dataset is one of mnist/cifar10. --out-dataset is one of kmnist/svhn/cifar100. To run ARPL+CS, add --cs after this command.

Evaluation

To evaluate the trained model for Open Set Classification Rate (OSCR) and Out-of-Distribution (OOD) detection setting, add --eval after the training command.

3. Results

We visualize the deep feature of Softmax/GCPL/ARPL/ARPL+CS as below.

Colored triangles represent the learned reciprocal points of different known classes.

4. PKU-AIR300

A new large-scale challenging aircraft dataset for open set recognition: Aircraft 300 (Air-300). It contains 320,000 annotated colour images from 300 different classes in total. Each category contains 100 images at least, and a maximum of 10,000 images, which leads to the long tail distribution.

Citation

  • If you find our work or the code useful, please consider cite our paper using:
@inproceedings{chen2021adversarial,
    title={Adversarial Reciprocal Points Learning for Open Set Recognition},
    author={Chen, Guangyao and Peng, Peixi and Wang, Xiangqian and Tian, Yonghong},
    journal={arXiv preprint arXiv:2103.00953},
    year={2021}
}
  • All publications using Air-300 Dataset should cite the paper below:
@InProceedings{chen_2020_ECCV,
    author = {Chen, Guangyao and Qiao, Limeng and Shi, Yemin and Peng, Peixi and Li, Jia and Huang, Tiejun and Pu, Shiliang and Tian, Yonghong},
    title = {Learning Open Set Network with Discriminative Reciprocal Points},
    booktitle = {The European Conference on Computer Vision (ECCV)},
    month = {August},
    year = {2020}
}