Spatial Location Constraint Prototype Loss for Open Set Recognition

Official PyTorch implementation of "Spatial Location Constraint Prototype Loss for Open Set Recognition".

More our open set recognition article and code information can be seen in https://arxiv.org/abs/2108.04225.

1. Requirements

Environments

These codes are supposed to be run with a Linux system. If you use Windows system to run them, it may encounter some errors.

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 and unzip it.

• tiny_imagenet

2. Training

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 SLCPLoss/GCPLoss/Softmax.

--dataset is one of mnist/svhn/cifar10/cifar100/tiny_imagenet.

3. Results

We visualize the deep feature of Softmax/GCPL/SLCPL as below.

Before getting the figure above, you need to train the LeNet++ network, whose architecture is in "./models/model.py".

Citation

• If you find our work or the code useful, please consider cite our paper using:

@misc{xia2021spatial,
      title={Spatial Location Constraint Prototype Loss for Open Set Recognition}, 
      author={Ziheng Xia and Ganggang Dong and Penghui Wang and Hongwei Liu},
      year={2021},
      eprint={2110.11013},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}