https://github.com/roysubhankar/dwt-domain-adaptation
Code for paper "Unsupervised Domain Adaptation using Feature-Whitening and Consensus Loss" (CVPR 2019)
https://github.com/roysubhankar/dwt-domain-adaptation
cvpr2019 domain-adaptation pytorch whitening
Last synced: 5 months ago
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Code for paper "Unsupervised Domain Adaptation using Feature-Whitening and Consensus Loss" (CVPR 2019)
- Host: GitHub
- URL: https://github.com/roysubhankar/dwt-domain-adaptation
- Owner: roysubhankar
- Created: 2019-04-04T16:39:53.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2021-03-11T15:19:23.000Z (over 4 years ago)
- Last Synced: 2024-08-04T03:09:22.638Z (about 1 year ago)
- Topics: cvpr2019, domain-adaptation, pytorch, whitening
- Language: Python
- Homepage:
- Size: 27.3 KB
- Stars: 64
- Watchers: 5
- Forks: 19
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 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](http://openaccess.thecvf.com/content_CVPR_2019/html/Roy_Unsupervised_Domain_Adaptation_Using_Feature-Whitening_and_Consensus_Loss_CVPR_2019_paper.html) 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
- Office-Home: To run the experiments on the [OfficeHome](http://hemanthdv.org/OfficeHome-Dataset/) dataset first you need to download the dataset from [this](https://drive.google.com/file/d/0B81rNlvomiwed0V1YUxQdC1uOTg/view) 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](https://drive.google.com/file/d/1Iw3pCXdiAiJJnZDzh7UToBNQipIVeMS2/view?usp=sharing).
```
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
```
- USPS -> MNIST:
```
python usps_mnist.py --group_size 4 --source 'usps' --target 'mnist'
```
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}
}
```
[](https://github.com/roysubhankar)