Multi-source Heterogeneous Domain Adaptation with Conditional Weighting Adversarial Network
- python 3.6
- tensorflow-gpu 1.4
- CUDA 8.0
- cudnn 6.0
- numpy
- scipy
- matplotlib
- scikit_learn
$ conda create -n cwan python=3.6
$ conda activate cwan
$ pip install tensorflow-gpu==1.4
$ conda install cudatoolkit=8.0
$ conda install cudnn=6.0
$ conda install scipy
$ conda install matplotlib
$ conda install scikit-learn
$ conda install matplotlib
You can download the example dataset from here (Password: 8i7c), and put in the folder of datasets.
All of the datasets can be downloaded from here (Password: 4y1q).
- You can run this code by inputing:
python -W ignore main.py
The results should be close to 59.67 (A (D_{4096}), D (R_{2048}) -> W (S_{800})). Note that different environmental outputs may be different.
- You can use your datasets by replacing:
source_exp = [ad.SAD, ad.SDR]
target_exp = [ad.TWS]
-
You can tune the parameters, i.e., lr_1 (learning rate for g(\dot), f(\dot)), lr_2 (learning rate for d(\dot)), T, d, beta, tau, for different applications.
-
The default parameters are: lr_1 = 0.004, lr_2 = 0.001, T = 500, d = 256, beta = 0.03, tau = 0.004.
If you find this helpful, please cite:
@article{DBLP:journals/corr/abs-2008-02714,
author = {Yuan Yao and
Xutao Li and
Yu Zhang and
Yunming Ye},
title = {Multi-source Heterogeneous Domain Adaptation with Conditional Weighting
Adversarial Network},
journal = {CoRR},
volume = {abs/2008.02714},
year = {2020},
url = {https://arxiv.org/abs/2008.02714},
archivePrefix = {arXiv},
}