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}
}
The implementation is built on the pytorch implementation of SSDA_MME and APE
You can download the datasets by following the instructions in 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---
|
...
- 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)
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent