https://github.com/wnjxyk/record
Demo for the paper "RECORD: Resource Constrained Semi-Supervised Learning under Distribution Shift"
https://github.com/wnjxyk/record
Last synced: over 1 year ago
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Demo for the paper "RECORD: Resource Constrained Semi-Supervised Learning under Distribution Shift"
- Host: GitHub
- URL: https://github.com/wnjxyk/record
- Owner: WNJXYK
- Created: 2020-05-31T08:28:34.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2020-05-31T10:04:48.000Z (about 6 years ago)
- Last Synced: 2025-01-20T08:11:42.599Z (over 1 year ago)
- Language: Python
- Homepage:
- Size: 12.7 KB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# RECORD
This is a demo for the paper: "RECORD: Resource Constrained Semi-Supervised Learning under Distribution Shift".
##### Prerequisites
* Python 3.7
* Pytorch
* scikit-learn
* h5py
* matplotlib
##### Folders
* src: Python source code.
* data: Placeholder for the dataset. Please download the dataset from [Google Drive](https://drive.google.com/drive/folders/1rWSbgKLkBMI1ZeBDNaXBvnkDIwBP5X8U).
* logs: Placeholder for the running logs.
* images: Placeholder for the line charts.
##### To Run
For example, you can run the following command in the root path:
```
python ./src/run.py
```
The result will be saved in logs folder with a line chart saved in the images folder.
In this demo, we prepared four benchmark data sets for the distribution shift and three implements of semi-supervised learning methods.
You can also use command `python ./src/run.py -h` to list the usages.
Contact wnjxyk@gmail.com for more questions.