• Python 3.6
• Pytorch 1.3

The links of datasets will be released afterwards,

• Office-Caltech
• NUSWIDE-ImageNet
• Multilingual Reuters Collection

The general command for training is,

python3 train.py

Change arguments for different experiments:

• dataset: "office" / "nusimg" / "mrc"
• batch_size: mini_batch size
• beta: The ratio of known target sample and Unk target sample in the pseudo label set
• num_layers: GNN's depth
• edge_loss: edge classification loss
• dis_loss: discrepancy loss
• c_loss: clustering embedding loss

For the detailed hyper-parameters setting for each dataset, please refer to Section 5.2 and Appendix 3.

Remember to change dataset_root to suit your own case

The training loss and validation accuracy will be automatically saved in './logs/', which can be visualized with tensorboard. The model weights will be saved in './checkpoints'