tntek/N2DCX
Official implementation for [N2DCX] Nearest Neighborhood-Based Deep Clustering for Source Data-absent Unsupervised Domain Adaptation
Official implementation for [N2DCX]([2107.12585] Nearest Neighborhood-Based Deep Clustering for Source Data-absent Unsupervised Domain Adaptation (arxiv.org))
Code (pytorch) for ['Nearest Neighborhood-Based Deep Clustering for Source Data-absent Unsupervised Domain Adaptation']([2107.12585] Nearest Neighborhood-Based Deep Clustering for Source Data-absent Unsupervised Domain Adaptation (arxiv.org)) on Office-31, Office-Home, VisDA-C. This article is under review.
Framework
Datasets and Prerequisites
You need to download the Office-31, Office-Home, VisDA-C dataset, modify the path of images in each '.txt' under the folder './data/'.
The experiments are conducted on one GPU (NVIDIA RTX TITAN).
- python == 3.7.3
- pytorch ==1.6.0
- torchvision == 0.7.0
Training and evaluation
- First training model on the source data, VisDA-C dataset is shown here.
cd ./object ~/anaconda3/bin/python N2DC_source.py --trte val --output ckps2020r0/source/ --da uda --gpu_id 0 --dset VISDA-C --net resnet101 --lr 1e-3 --max_epoch 10 --s 0
- Then adapting source model to target domain, with only the unlabeled target data.
# train the target domain by N2DC ~/anaconda3/bin/python N2DC_target.py --cls_par 0.2 --da uda --dset VISDA-C --gpu_id 0 --s 0 --t 1 --output_src ckps2020r0/source/ --output ckps2020r0/target_n2dc/ --net resnet101 --lr 1e-3 --seed 2020 # train the target domain by N2DC-EX ~/anaconda3/bin/python N2DCEX_target.py --cls_par 0.2 --da uda --dset VISDA-C --gpu_id 0 --s 0 --t 1 --output_src ckps2020r0/source/ --output ckps2020r0/target_n2dcex/ --net resnet101 --lr 1e-3 --seed 2020
Please refer to ./object/run.sh for all the settings for different methods and scenarios.
Results
The results of N2DCX is display under the folder './object/results/'.
Citation
If you find this code useful for your research, please cite our paper
@Article{tang2021n2dcx,
title={Nearest Neighborhood-Based Deep Clustering for Source Data-absent Unsupervised Domain Adaptation},
author={Song Tang, Yan Yang, Zhiyuan Ma, Norman Hendrich, Fanyu Zeng, Shuzhi Sam Ge, Changshui Zhang, Jianwei Zhang},
year={2021},
journal={arXiv:2107.12585},
url= {https://arxiv.org/abs/2107.12585}
}
Acknowledgement
DeepCluster(ECCV 2018)'s work.
SHOT (ICML 2020, also source-free)'s work.