https://github.com/kremerj/relabeling
Code repository for the robust active label correction paper.
https://github.com/kremerj/relabeling
active-learning aistats-2018 convolutional-neural-networks label-noise logistic-regression
Last synced: 8 months ago
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Code repository for the robust active label correction paper.
- Host: GitHub
- URL: https://github.com/kremerj/relabeling
- Owner: kremerj
- Created: 2018-03-14T16:07:34.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2018-04-12T14:25:52.000Z (about 8 years ago)
- Last Synced: 2025-03-18T05:44:21.872Z (about 1 year ago)
- Topics: active-learning, aistats-2018, convolutional-neural-networks, label-noise, logistic-regression
- Language: Terra
- Homepage:
- Size: 15.6 MB
- Stars: 10
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Robust Active Label Correction
This is the code repository complementing the paper
Jan Kremer, Fei Sha, and Christian Igel. [Robust Active Label Correction](http://proceedings.mlr.press/v84/kremer18a.html). *PMLR: Volume 84 (AISTATS)*, 2018
```
@inproceedings{Kremer18,
author = {J. Kremer and F. Sha and C. Igel},
title = {Robust Active Label Correction},
booktitle = {Proceedings of the 21st International Conference on Artificial Intelligence and Statistics},
series = {Proceedings of Machine Learning Research},
year = 2018,
volume = 84,
publisher = {PMLR}
}
```
Please cite us if you use any of the code provided here. All experiments from the paper can be reproduced from this repository. We use Python 3 and tensorflow 1.4. You can create and activate the conda environment by running
```
conda env create --file environment/relabeling.yml
source activate relabeling
```
or if you have GPU support
```
conda env create --file environment/relabeling-gpu.yml
source activate relabeling-gpu
```
To get the necessary data and the pretrained [model weights](http://www.cs.toronto.edu/~guerzhoy/tf_alexnet/) for the CNN experiment, run
```
sh scripts/fetch_model.sh
```
and get the data (images and annotions) from http://bit.ly/2Duy6nK and should be unpacked into ```data/baidu```.
The necessary Cython code can be compiled by calling
```
sh scripts/compile.sh
```
The logistic regression experiments can be reproduced by calling
```
sh scripts/relabeling.sh
```
The results can be found in ```output/experiment/std```.
The CNN experiments can be reproduced by calling
```
sh scripts/relabeling_deep.sh
```
The results can be found in ```output/experiment/deep```.
All plots can be generated by calling
```
sh scripts/generate_plots.sh
```
The figures can be found in ```output/experiment/std/figures``` for the logistic regression experiments and in ```output/experiment/deep/figures``` for the CNN experiment.
A single experiment can be run by calling
```
python relabeling.py
```
The command-line help should guide you regarding available options.