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https://github.com/ErikEnglesson/GJS
The official code for the NeurIPS 2021 paper Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels (https://arxiv.org/abs/2105.04522)
https://github.com/ErikEnglesson/GJS
Last synced: 7 days ago
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The official code for the NeurIPS 2021 paper Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels (https://arxiv.org/abs/2105.04522)
- Host: GitHub
- URL: https://github.com/ErikEnglesson/GJS
- Owner: ErikEnglesson
- Created: 2021-10-26T07:10:36.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2021-12-29T18:29:46.000Z (almost 3 years ago)
- Last Synced: 2024-08-02T15:31:01.384Z (3 months ago)
- Language: Python
- Homepage:
- Size: 47.9 KB
- Stars: 21
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels
The official code for the NeurIPS 2021 paper [Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels](https://arxiv.org/abs/2105.04522)
## Environment Setup
Create conda environment, activate environment, and install additional pip packages
```bash
conda env create -f gjs_env.yml -n gjs
conda activate gjs
python -m pip install -r requirements.txt
```
## Running Experiments
Please check scripts/ folder for yaml files corresponding to different experiments.For example, to run JS on 40% symmetric noise on the full CIFAR-10 training set, run the following
```bash
python train.py -c scripts/C10/sym/js-40.yaml \
--data_dir /path/to/dataset/
```
or GJS on 20% asymmetric noise on CIFAR-100
```bash
python train.py -c scripts/C100/asym/gjs-20.yaml \
--data_dir /path/to/dataset/
```
or GJS on WebVision
```bash
python train.py -c scripts/WebVision/gjs.yaml \
--data_dir /path/to/dataset/
```