chenjiehu / PGL

Progressive Graph Learning for Open-Set Domain Adaptation

Requirements

• Python 3.6
• Pytorch 1.3

Datasets

The links of datasets will be released afterwards,

• Syn2Real-O (VisDA-18)
• VisDA-17
• Office-home

Training

The general command for training is,

python3 train.py

Change arguments for different experiments:

• dataset: "home" / "visda" / "visda18"
• batch_size: mini_batch size
• beta: The ratio of known target sample and Unk target sample in the pseudo label set
• EF : Enlarging Factor α
• num_layers: GNN's depth
• adv_coeff: adversarial loss coefficient γ
• node_loss: node classification loss μ 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'

TODO List

• Update the GradReverse layer for Pytorch 1.4

• Update detail config file for datasets

  • VisDA-18
  • VisDA-17
  • Office-home

• Fix progress bar