Domain Whitening Transform for Unsupervised Domain Adaptation (CVPR 2019)

Official PyTorch github repository for the paper Unsupervised Domain Adaptation using Feature-Whitening and Consensus Loss published in The Conference on Computer Vision and Pattern Recognition (CVPR) held at Long Beach, California in June, 2019.

Prerequisites

• Pytorch 1.0
• Python 3.5

Usage

To run the experiments on the OfficeHome dataset first you need to download the dataset from this page. Following this step, you would need to download the ResNet50 pre-trained checkpoint, trained on ImageNet with the BatchNorm layers (in the first conv layer and the first Res block) replaced by whitening normalization layers. The pre-trained weights is available here.

$ python resnet50_dwt_mec_officehome.py --s_dset_path path-to-source-dataset-folder --t_dset_path path-to-target-dataset folder --resnet_path path-to-pre-trained-resnet50-weights

If you find this code useful for your research, please cite our paper:

@article{roy2019unsupervised,
  title={Unsupervised Domain Adaptation using Feature-Whitening and Consensus Loss},
  author={Roy, Subhankar and Siarohin, Aliaksandr and Sangineto, Enver and Bulo, Samuel Rota and Sebe, Nicu and Ricci, Elisa},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2019}
}