https://github.com/smkim7-kr/AdaMatch-pytorch
Unofficial implementation of "AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation"
https://github.com/smkim7-kr/AdaMatch-pytorch
computer-vision deep-learning domain-adaptation semi-supervised-domain-adaptation semi-supervised-learning unsupervised-domain-adaptation
Last synced: about 1 year ago
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Unofficial implementation of "AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation"
- Host: GitHub
- URL: https://github.com/smkim7-kr/AdaMatch-pytorch
- Owner: smkim7-kr
- License: mit
- Created: 2021-09-08T16:51:30.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2021-09-08T17:10:12.000Z (almost 5 years ago)
- Last Synced: 2024-11-15T06:32:05.937Z (over 1 year ago)
- Topics: computer-vision, deep-learning, domain-adaptation, semi-supervised-domain-adaptation, semi-supervised-learning, unsupervised-domain-adaptation
- Language: Python
- Homepage:
- Size: 2.98 MB
- Stars: 8
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# AdaMatch-pytorch
This is an unofficial implementation of [AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation](https://arxiv.org/abs/2106.04732). Official code is [here](https://github.com/google-research/adamatch) written by Google Research with Jax. [Paper summary](https://smkim7.notion.site/AdaMatch-A-Unified-Approach-to-Semi-Supervised-Learning-and-Domain-Adaptation-Korean-9e6e221cca5e46cb80b9d36e6153553c) and [video presentation](https://www.youtube.com/watch?v=VMZZNaHSTf4&t=162s) are done by myself (in Korean unfortunately).
Step-by-step explanations in Colab notebooks are [here](https://colab.research.google.com/drive/1FY67_4dzLxIcWVkJR6IzwhzKWBcfesO9#scrollTo=SRFCRNEoNerZ).
### Requirements
You can easily install all requirements by the command
```
pip install -r requirements.txt
```
### Datasets
The code supports source to target domain adaptation from SVHN to MNIST (part of DigitFive dataset presented in the paper) .
### Training
```
python main.py --uratio 3 --tau 0.9
```
The code includes different hyperparameters for config including
* uratio (default=3): Ratio between source and target batch size (uratio * source = target)
* tau (default=0.9): Pseudolabel threshold for Relative confidence threshold
Default all follows from the paper.
### References
* [AdaMatch-pytorch](https://github.com/zysymu/AdaMatch-pytorch) by zysymu
```
@article{berthelot2021adamatch,
title={AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation},
author={Berthelot, David and Roelofs, Rebecca and Sohn, Kihyuk and Carlini, Nicholas and Kurakin, Alex},
journal={arXiv preprint arXiv:2106.04732},
year={2021}
}
```