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DEMS (Data-free Exploitation of Multiple Sources)

This repository is a PyTorch implementation of "Unsupervised Multi-Source Domain Adaptation with No Observable Source Data" which is submitted to PLOS ONE.

Requirements

To install requirements:

pip install -r requirements.txt

CUDA settings are highly encouraged for the efficient training of DEMS which is constructed based on CNN structure. Detailed instruction for CUDA-based PyTorch settings is described in https://pytorch.org/get-started/previous-versions/.

Pre-processed Datasets

For easy usage, we provide pre-processed datasets of {MNIST, MNIST-M, SVHN, SynDigits, USPS} which is uploaded in https://doi.org/10.6084/m9.figshare.14790036.v1.

The datasets should be placed in this package as follows:

DEMS
└───README.md
│
└───demo.sh
│
└───requirements.txt
│
└───docs
│   └─── ...
│
└───src
│   └─── ...
│
└───out
│   └─── ...
│
└───pretrained
│   └─── ...   
│
└───data
    └───digit
        └───MNIST   
        └───MNISTM
        └───SVHN
        └───SYNDIGITS
        └───USPS

Pre-trained Classifiers

Each pre-trained classifier for <source_domain>, which is necessary for training and testing DEMS, is provided in ./pretrained/<source_domain>.pt.

Demo

You can run the demo script by bash demo.sh, which simply runs src/main.py with USPS target in training mode.

Training

Run this command to train DEMS:

python main.py --target <target_domain> --train True

Train DEMS and save the best model to ./out/<target_domain>/adaptation_models.pt

<target_domain> should be one of whole datasets: {mnist, mnistm, svhn, syndigits, usps}.

The remains excluding <target_domain> among the whole datasets are selected as sources automatically.

The optimal hyperparameters are set as default.

  • epsilon: 0.9
  • temperature (lambda in the paper): 1.0

Evaluation

Run this command to test DEMS:

python main.py --target <target_domain> --train False

Load a saved model from ./out/<target_domain>/adaptation_models.pt and test the model on the target test dataset.

A pre-trained adaptation network is needed to run this code. We thus provide the pre-trained adaptation network for users that want to evaluate DEMS instantly without any training process.

<target_domain> should be one of whole datasets: {mnist, mnistm, svhn, syndigits, usps}.

The remains excluding <target_domain> among the whole datasets are selected as sources automatically.

Results

DEMS achieves the following classification performance:

Target dataset MNIST MNIST-M SVHN SynDigits USPS
Accuracy 98.64% 82.20% 77.22% 94.57% 95.57%

Reference

You can copy the following information to cite the paper:

@article{jeon2021unsupervised,
  title={Unsupervised multi-source domain adaptation with no observable source data},
  author={Jeon, Hyunsik and Lee, Seongmin and Kang, U},
  journal={Plos one},
  volume={16},
  number={7},
  pages={e0253415},
  year={2021},
  publisher={Public Library of Science San Francisco, CA USA}
}

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