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https://github.com/zhoudw-zdw/CVPR21-Proser
Learning Placeholders for Open-Set Recognition (CVPR'21 Oral)
https://github.com/zhoudw-zdw/CVPR21-Proser
open-set-recognition open-world
Last synced: about 1 month ago
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Learning Placeholders for Open-Set Recognition (CVPR'21 Oral)
- Host: GitHub
- URL: https://github.com/zhoudw-zdw/CVPR21-Proser
- Owner: zhoudw-zdw
- Created: 2021-07-16T04:32:03.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2021-07-23T08:37:44.000Z (over 3 years ago)
- Last Synced: 2024-08-04T03:09:50.169Z (5 months ago)
- Topics: open-set-recognition, open-world
- Language: Python
- Homepage: https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Learning_Placeholders_for_Open-Set_Recognition_CVPR_2021_paper.pdf
- Size: 108 KB
- Stars: 77
- Watchers: 1
- Forks: 8
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
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README
# Learning Placeholders for Open-Set Recognition (Proser)
The code repository for "Learning Placeholders for Open-Set Recognition
" [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Learning_Placeholders_for_Open-Set_Recognition_CVPR_2021_paper.pdf) (CVPR21) in PyTorch. If you use any content of this repo for your work, please cite the following bib entry:
@inproceedings{zhou2021learning,
author = {Zhou, Da-Wei and Ye, Han-Jia and Zhan, De-Chuan},
title = {Learning Placeholders for Open-Set Recognition},
booktitle = {CVPR},
pages = {4401-4410},
year = {2021}
}
## Learning Placeholders for Open-Set Recognition
Traditional classifiers are deployed under closed-set setting, with both training and test classes belong to the same set. However, real-world applications probably face the input of unknown categories, and the model will recognize them as known ones. Under such circumstances, open-set recognition is proposed to maintain classification performance on known classes and reject unknowns. The closed-set models make overconfident predictions over familiar known class instances, so that calibration and thresholding across categories become essential issues when extending to an open-set environment. To this end, we proposed to learn PlaceholdeRs for Open-SEt Recognition (Proser), which prepares for the unknown classes by allocating placeholders for both data and classifier. In detail, learning data placeholders tries to anticipate open-set class data, thus transforms closed-set training into open-set training. Besides, to learn the invariant information between target and non-target classes, we reserve classifier placeholders as the class-specific boundary between known and unknown. The proposed Proser efficiently generates novel class by manifold mixup, and adaptively sets the value of reserved open-set classifier during training. Experiments on various datasets validate the effectiveness of our proposed method.
## Prerequisites
The following packages are required to run the scripts:
- [PyTorch-1.4 and torchvision](https://pytorch.org)
- Dataset: please download the dataset and unzip it into the folder data/cifar
- Pre-trained weights: we provide [our pretrained weights](https://drive.google.com/file/d/1Pwnetsy59fXRTgeYhUp5S0k-EqDuQ-CF/view?usp=sharing) for this trial. Download and put it to 'results/Dcifar10_relabel-Msoftmax-BWideResnet/LR0.1-K[8 4 7 2 1 9]-U[3 0 6 5]-Seed9/' You can also use the pretrain code for other tasks. See below for details.
## Dataset
### CIFAR10
Download [CIFAR10 matlab version](https://www.cs.toronto.edu/~kriz/cifar.html) and unzip it to data/cifar/
## Code Structures
There are four parts in the code.
- `models`: It contains the backbone network for the experiment
- `data`: Images and splits for the data sets, as well as the data preprocessing code.
- `results`: The pre-trained weights of different networks.
## Unknown Detection
We provide the code to reproduce results on CIFAR 10 unknown detection task, c.f. Table.1 in the main paper.
- Download the pretrained weights:
- Train the Proser model
```
python proser_unknown_detection.py
```
and you will get corresponding AUC measures.
## Acknowledgment
We thank the following repos providing helpful components/functions in our work.
- [pytorch-cifar](https://github.com/kuangliu/pytorch-cifar)
- [FEAT](https://github.com/Sha-Lab/FEAT)
## Contact
If there are any questions, please feel free to contact with the author: Da-Wei Zhou ([email protected]). Enjoy the code.