Code release for Representation Subspace Distance for Domain Adaptation Regression (ICML 2021)
- Python3
- PyTorch == 0.4.1 (with suitable CUDA and CuDNN version)
- torchvision == 0.2.1
- Numpy
- argparse
- PIL
dSprites can be downloaded here:
"color.tgz", "https://cloud.tsinghua.edu.cn/f/9ce9f2abc61f49ed995a/?dl=1",
"noisy.tgz", "https://cloud.tsinghua.edu.cn/f/674435c8cb914ca0ad10/?dl=1",
"scream.tgz", "https://cloud.tsinghua.edu.cn/f/0613675916ac4c3bb6bd/?dl=1".
MPI3D can be downloaded here:
"real.tgz", "https://cloud.tsinghua.edu.cn/f/04c1318555fc4283862b/?dl=1",
"realistic.tgz", "https://cloud.tsinghua.edu.cn/f/2c0f7dacc73148cea593/?dl=1",
"toy.tgz", "https://cloud.tsinghua.edu.cn/f/6327912a50374e20af95/?dl=1".
Datalists are in the corresponding folder.
You can reproduce the results by runing rsd.sh in each folder.
If you use this code for your research, please consider citing:
@inproceedings{DAR_ICML_21,
title={Representation Subspace Distance for Domain Adaptation Regression},
author={Chen, Xinyang and Wang, Sinan and Wang, Jianmin and Long, Mingsheng},
booktitle={International Conference on Machine Learning},
pages={1749--1759},
year={2021}
}
If you have any problem about our code, feel free to contact chenxinyang95@gmail.com.