If you have any questions, feel free to pose an issue or send an email to [email protected]. We are always happy to receive feedback!
The code for TSDA is developed based on CIDA. CIDA also provides many baseline implementations (e.g., DANN, MDD), which we used for performance comparasion in our paper. Please refer to its code for details. For baseline GRDA, please refer to this code.
python default_run.py -c config_tree_14 (or)
python default_run.py --config config_tree_14
python default_run.py -c config_tree_40 (or)
python default_run.py --config config_tree_40
We use visdom to visualize. We assume the code is run on a remote gpu machine.
Find the config in "config" folder. Choose the config you need and Set "opt.use_visdom" to "True".
python -m visdom.server -p 2000
Now connect your computer with the gpu server and forward the port 2000 to your local computer. You can now go to: http://localhost:2000 (Your local address) to see the visualization during training.