Code release for Discriminative Adversarial Domain Adaptation (AAAI2020).
The paper is avaliable here.
- Python 3.6.8
- Pytorch 1.0.0
The structure of the dataset should be like
VisDA
|_ visda_train
| |_ aeroplane
| |_ <im-1-name>.jpg
| |_ ...
| |_ <im-N-name>.jpg
| |_ bicycle
| |_ <im-1-name>.jpg
| |_ ...
| |_ <im-N-name>.jpg
| |_ ... (omit 9 classes)
| |_ truck
| |_ <im-1-name>.jpg
| |_ ...
| |_ <im-N-name>.jpg
|_ visda_validation_first6classes
| |_ aeroplane
| |_ <im-1-name>.jpg
| |_ ...
| |_ <im-N-name>.jpg
| |_ bicycle
| |_ <im-1-name>.jpg
| |_ ...
| |_ <im-N-name>.jpg
| |_ ... (omit 3 classes)
| |_ knife
| |_ <im-1-name>.jpg
| |_ ...
| |_ <im-N-name>.jpg
|_ ...
Replace paths and domains in run_office31_partial.sh with those in your own system.
Replace paths and domains in run_visda_partial.sh with those in your own system.
@InProceedings{dada,
title={Discriminative Adversarial Domain Adaptation},
author={Hui Tang and Kui Jia},
booktitle={Association for the Advancement of Artificial Intelligence (AAAI)},
year={2020},
}
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent