• [2024-7] Now we support arbitrary number of views for multi-view clustering and classification tasks. See run_*_multiview.py and model_multiview.py for more details. Remember to set the number of views and network architecture in configure/configure_*_multiview.py. By default, View 0 is set as the core view in our code.

python run_clustering_multiview.py --missing_rate 0.5
python run_supervised_multiview.py --missing_rate 0.5

This repo contains the code and data of our IEEE TPAMI'2022 paper Dual Contrastive Prediction for Incomplete Multi-view Representation Learning. Precise numerical results of different missing rates could be accessed from Results_missing_rate.xlsx.

Dual Contrastive Prediction for Incomplete Multi-view Representation Learning

COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction

pytorch>=1.2.0

numpy>=1.19.1

scikit-learn>=0.23.2

munkres>=1.1.4

The hyper-parameters, the training options are defined in the configure folder.

• configure_clustering.py: bi-view data clustering
• configure_clustering_multiview.py: 3-view data clustering
• configure_supervised.py: bi-view data classification (including human action recognition)
• configure_supervised_multiview.py: 3-view data classification

Note that for multi-view setting, we place both complete graph and cove view setting (i.e., type='CG' or 'CV' ).

The Caltech101-20, LandUse-21, Scene-15, UWA, and DHA datasets are placed in "data" folder. The NoisyMNIST dataset could be downloaded from cloud.

The code includes:

• an example implementation of the model. The network structure and training/evaluation pipeline are in model.py and model.multiview.py:

• clustering tasks for different missing rates.

python run_clustering.py --dataset 0 --devices 0 --print_num 100 --test_time 5 --missing_rate 0.5
python run_clustering_multiview.py 

• classification tasks for different missing rates.

python run_supervised.py --dataset 0 --devices 0 --print_num 100 --test_time 5 --missing_rate 0.5
python run_supervised_multiview.py

• human action recognition tasks

python run_HAR.py 

You can get the following output by runing python run_HAR.py:

Epoch : 100/2000 ===> Reconstruction loss = 5.1242===> Reconstruction loss = 0.0489 ===> Map loss = 0.0001 ===> Map loss = 0.0001 ===> Loss_icl = -7.4860e+01 ===> Loss_ccl = 1.2800e+02 ===> All loss = 5.3657e+01
RGB   Accuracy on the test set is 0.6653
Depth Accuracy on the test set is 0.3926
RGB+D Accuracy on the test set is 0.8430
onlyRGB Accuracy on the test set is 0.6860
onlyDepth Accuracy on the test set is 0.3636
Epoch : 2000/2000 ===> Reconstruction loss = 4.3108===> Reconstruction loss = 0.0163 ===> Map loss = 0.0001 ===> Map loss = 0.0004 ===> Loss_icl = -7.7413e+01 ===> Loss_ccl = 1.2800e+02 ===> All loss = 5.1020e+01
RGB   Accuracy on the test set is 0.7769
Depth Accuracy on the test set is 0.8306
RGB+D Accuracy on the test set is 0.8926
onlyRGB Accuracy on the test set is 0.7727
onlyDepth Accuracy on the test set is 0.8182

If you find our work useful in your research, please consider citing:

@ARTICLE{9852291,
  author={Lin, Yijie and Gou, Yuanbiao and Liu, Xiaotian and Bai, Jinfeng and Lv, Jiancheng and Peng, Xi},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Dual Contrastive Prediction for Incomplete Multi-View Representation Learning}, 
  year={2022},
  doi={10.1109/TPAMI.2022.3197238}
}
@inproceedings{lin2021completer,
   title={COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction},
   author={Lin, Yijie and Gou, Yuanbiao and Liu, Zitao and Li, Boyun and Lv, Jiancheng and Peng, Xi},
   booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
   month={June},
   year={2021}
}