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Code for our paper Secure Domain Adaptation with Multiple Choices.

If you find our codebase relevant in your work, please consider citing our paper at

@article{stan2022secure,
	title={Secure Domain Adaptation with Multiple Sources},
	author={Serban Stan and Mohammad Rostami},
	journal={Transactions on Machine Learning Research},
	year={2022},
	url={https://openreview.net/forum?id=UDmH3HxxPp}
}

Our code builds on the implementations in https://sites.google.com/view/simpal (Your Classifier can Secretly Suffice Multi-Source Domain Adaptation) ,https://github.com/jindongwang/transferlearning (Transfer Learning), https://github.com/eifuentes/swae-pytorch (Sliced Wasserstein Autoencoder) and https://github.com/driptaRC/DECISION (Unsupervised Multi-source Domain Adaptation Without Access to Source Data) .

Pre-trained features should be added under data/pretrained-features. These features can be generated automatically via the code in code/feature-gen-code once the data/ folder has been populated. Due to space constraints for this submission, we only provide pre-trained features for one of the datasets.

To run experiments, simply go to code/, and call run_pipeline.py . An example run can take the form:

python run_pipeline.py --dataset image-clef --num_exp 3 --gpu_id 0

More documentation on how to use this script can be found inside run_pipeline.

  • main.py calls the E2E training routine, and then calls evaluate_model which computes target results
  • config.py and config_populate.py correspond to populating necessary information regarding dataset (#classes, network architecture, im. resolution etc.)
  • exp_select.py is an addition to config, and is responsible for selecting the experiment to be run.
  • evaluate_model.py generates target results for different choices of w
  • trainer.py implements the MultiSourceTrainer class, which first trains a model on source data, then performs adaptation. This file represents the bulk of the algorithm described in the manuscript.
  • guassian_utils.py implements logic related to learning and sampling from the intermediate GMM distribution
  • runner.py allows running a single task for a dataset. For example python runner.py --dataset image-clef --task PC_I --gpu_id 0 --num_exp 5
  • dataset.py implements reading in a dataset into pytorch
  • metrics.py implements wasserstein distance alongside accuracy metrics
  • net.py implements the network logic
  • customlayers.py implements the individual network modules (e.g. fully connected module, classifier module etc.)

Following a succesful run, dataset statistics can be found in the summaries/ folder, and model weights can be found in the weights/ folder. The current code allows the model to train several tasks in parallel accross multiple gpus.