A PyTorch suite to systematically evaluate different domain adaptation methods.

• Python3
• Pytorch==1.7
• Numpy==1.20.1
• scikit-learn==0.24.1
• Pandas==1.2.4
• skorch==0.10.0 (For DEV risk calculations)
• openpyxl==3.0.7 (for classification reports)
• Wandb=0.12.7
• Hydra=1.2.0
• OmegaConf=2.2.3

1. Clone repository

git clone [email protected]:<repo>
cd bpda

2. Create a python 3 conda environment

conda env create -f environment.yml

4. Ensure that all required temp directories are available

• data

We used four public datasets in this study. We also provide the preprocessed versions as follows:

• Moons
• Amazon Reviews
• DomainNet
• Sleep-EDF
• UCIHAR
• HHAR
• WISDM

To add new dataset (e.g., NewData), it should be placed in a folder named: NewData in the datasets directory.

Since "NewData" has several domains, each domain should be split into train/test splits with naming style as "train_x.pt" and "test_x.pt".

The structure of data files should in dictionary form as follows: train.pt = {"samples": data, "labels: labels}, and similarly for test.pt.

Next, you have to add a class with the name NewData in the configs/data_model_configs.py file. You can find similar classes for existing datasets as guidelines. Also, you have to specify the cross-domain scenarios in self.scenarios variable.

Last, you have to add another class with the name NewData in the configs/hparams.py file to specify the training parameters.

• Deep Coral
• MMDA
• DANN
• CDAN
• DIRT-T
• DSAN
• HoMM
• DDC
• CoDATS
• AdvSKM
• CMD

To add a new algorithm, place it in algorithms/algorithms.py file.

./run.sh

./collect_results.sh

Main trainer file is trainer.py and includes also source-only results when executed.

• Each run will have all the cross-domain scenarios results in the format runx_src_to_trg, where x is the run_id.
• Under each directory, you will find the classification report, a log file, checkpoint, and the different risks scores.
• By the end of the all the runs, you will find the overall average and std results in the run directory.

• Moment Matching for Multi-Source Domain Adaptation
• Amazon product data
• Unsupervised Domain Adaptation by Backpropagation
• Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation
• The balancing principle for parameter choice in distance-regularized domain adaptation

@inproceedings{
  IWA23,
  title={Addressing Parameter Choice Issues in Unsupervised Domain Adaptation by Aggregation},
  author={Dinu, Marius-Constantin and Beck, Maximilian and Nguyen, Duc Hoan and Huber, Andrea and Eghbal-zadeh, Hamid and Moser, Bernhard A. and Pereverzyev, Sergei V. and Hochreiter, Sepp and Zellinger, Werner},
  booktitle={Submitted to The Eleventh International Conference on Learning Representations },
  year={2023},
  url={https://openreview.net/forum?id=M95oDwJXayG},
  note={under review}
}