DARE-GRAM

[CVPR 2023] Code for our paper DARE-GRAM : Unsupervised Domain Adaptation Regression by Aligning Inversed Gram Matrices

Prerequisites

• Python3
• Numpy
• PyTorch == 1.12.1 (with CUDA and CuDNN (cu113))
• torchvision == 0.13.1
• PIL
• scikit-learn

Please create and activate the following conda envrionment. To reproduce our results, please kindly create and use this environment.

# It may take several minutes for conda to solve the environment
conda update conda
conda env create -f environment.yml
conda activate daregram 

Train and Test DARE-GRAM model

The program can be run with the default parameters using the following:

#Train for dSprites
cd code/dsprites
sh dare_gram.sh

#Train for MPI3D
cd code/mpi3d
sh dare_gram.sh

Code was tested on a RTX 3090.

Citation

Please cite our work if you find it useful.

@inproceedings{nejjar2023domain,
  title={DARE-GRAM : Unsupervised Domain Adaptation Regression by Aligning Inversed Gram Matrices},
  author={Nejjar, Ismail and Wang, Qin and Fink, Olga},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.},
  year={2023}
}

Acknowledgement

• RSD is used as our codebase and our DA baseline official

Data links

dSprites can be downloaded from: here

MPI3D can be downloaded from here.

The files should be unziped and put in distinctive folders (template was provided).

Contact

For questions regarding the code, please contact ismail.nejjar@epfl.ch.