Open-world Semi-supervised Novel Class Discovery

[IJCAI 2023] Official code for Open-world Semi-supervised Novel Class Discovery (OpenNCD)

Introduction

Open-world scenario:

• Unseen novel classes mixed in unlabeled data in semi-supervised learning

Our tasks:

1. Recognize the known classes
2. Discover the novel classes
3. Estimate the number of the novel classes

Running

Requirements

Please refer to requirements.txt.

Pretrain Models

We use the unsupervised SimCLR for pretraining. The pretrained resnet-18 models can be found here (same as orca). Please unzip them to './pretrained'.

Scripts

• If the number of novel classes is pre-known, spectral clustering will be used for prototype grouping.
• To train on CIFAR-10 with 10% labeled data in known class data, run

python main.py --dataset cifar10 --labeled_num 5 --labeled_ratio 0.1  --save_log

• If the number of the novel class is unknown, ['propagation', 'connected', 'louvain'] can be used for prototype grouping and class number estimation, where ['louvain'] performs best in our experiments.
• To train on CIFAR-10 without a pre-defined number of classes, run

python main.py --dataset cifar10 --labeled_num 5 --labeled_ratio 0.1 --group_method louvain  --unknown_n_cls --save_log

Update

2023.8.25 Fix the bug when training with the known number of class

2023.8.10 Update main.py in the class number estimation part (--unknown_n_cls)

• Implement an adaptive range for EPS searching to facilitate progressive grouping
• Set the value of l_cls to 0 at the grouping stage to avoid over-fitting

Acknowledgements

Thanks for the excellent repositories orca and GCD.

Citation

If you find our code useful, please consider citing:

@inproceedings{ijcai2023p445,
  title     = {Open-world Semi-supervised Novel Class Discovery},
  author    = {Liu, Jiaming and Wang, Yangqiming and Zhang, Tongze and Fan, Yulu and Yang, Qinli and Shao, Junming},
  booktitle = {Proceedings of the Thirty-Second International Joint Conference on
               Artificial Intelligence, {IJCAI-23}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  pages     = {4002--4010},
  year      = {2023},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2023/445},
}