psycoder-sup/arpl

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

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.

• 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}
}