https://github.com/griffintaur/clda_neurips21
https://github.com/griffintaur/clda_neurips21
Last synced: 4 months ago
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- Host: GitHub
- URL: https://github.com/griffintaur/clda_neurips21
- Owner: Griffintaur
- License: gpl-3.0
- Created: 2022-08-02T07:31:03.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-08-06T13:09:50.000Z (over 2 years ago)
- Last Synced: 2024-12-27T16:51:31.174Z (4 months ago)
- Language: Python
- Size: 2.5 MB
- Stars: 10
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation [[Paper]](https://arxiv.org/abs/2107.00085) [[Website]](https://griffintaur.github.io/CLDA_NeurIPS/)
This repository contains the implementation details of our CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation (CLDA) approach for domain adaptation in images.
Ankit Singh , "CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation"
If you use the codes and models from this repo, please cite our work. Thanks!
```
@inproceedings{
singh2021clda,
title={{CLDA}: Contrastive Learning for Semi-Supervised Domain Adaptation},
author={Ankit Singh},
booktitle={Advances in Neural Information Processing Systems},
editor={A. Beygelzimer and Y. Dauphin and P. Liang and J. Wortman Vaughan},
year={2021},
url={https://openreview.net/forum?id=1ODSsnoMBav}
}
```
## Acknowledgments
The implementation is built on the pytorch implementation of [SSDA_MME](https://github.com/VisionLearningGroup/SSDA_MME) and [APE](https://github.com/TKKim93/APE)
### Dataset Structure
You can download the datasets by following the instructions in [SSDA_MME](https://github.com/VisionLearningGroup/SSDA_MME).
```
data---
|
multi---
| |
| Real
| Clipart
| Product
| Real
office_home---
| |
| Art
| Clipart
| Product
| Real
office---
| |
| amazon
| dslr
| webcam
txt---
|
multi---
| |
| labeled_source_images_real.txt
| unlabeled_target_images_real_3.txt
| labeled_target_images_real_3.txt
| unlabeled_source_images_sketch.txt
| ...
office---
| |
| labeled_source_images_amazon.txt
| unlabeled_target_images_amazon_3.txt
| labeled_target_images_amazon_3.txt
| unlabeled_source_images_webcam.txt
| ...
office_home---
|
...
```
### Example
#### Train
* DomainNet (clipart, painting, real, sketch)
```
python clda_final.py --dataset multi --source real --target sketch --save_interval 500 --steps 50000 --net resnet34 --num 3 --save_check
```
* Office-home (Art, Clipart, Product, Real)
* Office (amazon, dslr, webcam)