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https://github.com/LiJunnan1992/DivideMix
Code for paper: DivideMix: Learning with Noisy Labels as Semi-supervised Learning
https://github.com/LiJunnan1992/DivideMix
Last synced: 3 months ago
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Code for paper: DivideMix: Learning with Noisy Labels as Semi-supervised Learning
- Host: GitHub
- URL: https://github.com/LiJunnan1992/DivideMix
- Owner: LiJunnan1992
- License: mit
- Created: 2019-11-21T08:28:36.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2020-09-14T10:20:04.000Z (over 4 years ago)
- Last Synced: 2024-08-03T23:15:08.443Z (6 months ago)
- Language: Python
- Size: 121 KB
- Stars: 527
- Watchers: 9
- Forks: 82
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# DivideMix: Learning with Noisy Labels as Semi-supervised Learning
PyTorch Code for the following paper at ICLR2020:\
Title: DivideMix: Learning with Noisy Labels as Semi-supervised Learning [pdf]\
Authors:Junnan Li, Richard Socher, Steven C.H. Hoi\
Institute: Salesforce ResearchAbstract\
Deep neural networks are known to be annotation-hungry. Numerous efforts have been devoted to reduce the annotation cost when learning with deep networks. Two prominent directions include learning with noisy labels and semi-supervised learning by exploiting unlabeled data. In this work, we propose DivideMix, a novel framework for learning with noisy labels by leveraging semi-supervised learning techniques. In particular, DivideMix models the per-sample loss distribution with a mixture model to dynamically divide the training data into a labeled set with clean samples and an unlabeled set with noisy samples, and trains the model on both the labeled and unlabeled data in a semi-supervised manner. To avoid confirmation bias, we simultaneously train two diverged networks where each network uses the dataset division from the other network. During the semi-supervised training phase, we improve the MixMatch strategy by performing label co-refinement and label co-guessing on labeled and unlabeled samples, respectively. Experiments on multiple benchmark datasets demonstrate substantial improvements over state-of-the-art methods.Illustration\
Experiments\
First, please create a folder named checkpoint to store the results.\mkdir checkpoint
\
Next, run \python Train_{dataset_name}.py --data_path path-to-your-data
Cite DivideMix\
If you find the code useful in your research, please consider citing our paper:
@inproceedings{
li2020dividemix,
title={DivideMix: Learning with Noisy Labels as Semi-supervised Learning},
author={Junnan Li and Richard Socher and Steven C.H. Hoi},
booktitle={International Conference on Learning Representations},
year={2020},
}License\
This project is licensed under the terms of the MIT license.