https://github.com/nazmul-karim170/UNICON

[CVPR'22] Official Implementation of the CVPR 2022 paper "UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning"
https://github.com/nazmul-karim170/UNICON

contrastive-learning deep-learning deep-neural-networks jensen-shannon-divergence label-noise-robustness machine-learning noisy-labels semi-supervised-learning

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[CVPR'22] Official Implementation of the CVPR 2022 paper "UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning"

• Host: GitHub
• URL: https://github.com/nazmul-karim170/UNICON
• Owner: nazmul-karim170
• License: mit
• Created: 2022-03-26T00:29:13.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-12-31T01:26:28.000Z (10 months ago)
• Last Synced: 2024-07-22T05:20:55.826Z (4 months ago)
• Topics: contrastive-learning, deep-learning, deep-neural-networks, jensen-shannon-divergence, label-noise-robustness, machine-learning, noisy-labels, semi-supervised-learning
• Language: Python
• Homepage:
• Size: 877 KB
• Stars: 59
• Watchers: 2
• Forks: 14
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
 UNICON: Combating Label Noise Through Uniform Selection and Contrastive

Learning



 If you like our project, please give us a star ⭐ on GitHub for the latest update.  




[![arXiv](https://img.shields.io/badge/Arxiv-2312.09313-b31b1b.svg?logo=arXiv)](https://arxiv.org/pdf/2203.14542.pdf)

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://github.com/nazmul-karim170/UNICON-Noisy-Label/blob/main/LICENSE) 



## [Paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Karim_UniCon_Combating_Label_Noise_Through_Uniform_Selection_and_Contrastive_Learning_CVPR_2022_paper.pdf) 

## Training Pipeline

### UNICON Framework

![Framework](./Figure/Snip20220331_3.png)

### Installation Guide

1. Create a conda environment

	```bash

	conda create -n unicon 

	conda activate unicon

 	```

2. After creating a virtual environment, install the required packages 

	

 	```bash

	pip install -r requirements.txt

	```

  

### Download the Datasets

* For adding Synthetic Noise, download these datasets

	1. CIFAR10

 	2. CIFAR100

  	3. Tiny-ImageNet

* For Datasets with Real-World Label Noise

  	1. Clothing1M (Please contact tong.xiao.work[at]gmail[dot]com to get the download link)

  	2. WebVision

  

### UNICON Training

* Example run (CIFAR10 with 50% symmetric noise) 

	```bash

	python Train_cifar.py --dataset cifar10 --num_class 10 --data_path ./data/cifar10 --noise_mode 'sym' --r 0.5 

	```

 

* Example run (CIFAR100 with 90% symmetric noise) 

	```bash

	python Train_cifar.py --dataset cifar100 --num_class 100 --data_path ./data/cifar100 --noise_mode 'sym' --r 0.9 

	```

 

This will throw an error as downloaded files will not be in the proper folder. That is why they must be manually moved to the "data_path".

* Example Run (TinyImageNet with 50% symmetric noise)

	```bash

	python Train_TinyImageNet.py --ratio 0.5

	```

* Example run (Clothing1M)

   	```bash

	python Train_clothing1M.py --batch_size 32 --num_epochs 200   

	```

* Example run (Webvision)

   

	```bash

	python Train_webvision.py 

	```

### Reference 

If you have any questions, do not hesitate to contact [email protected]

Also, if you find our work useful please consider citing our work: 

	@InProceedings{Karim_2022_CVPR,

	    author    = {Karim, Nazmul and Rizve, Mamshad Nayeem and Rahnavard, Nazanin and Mian, Ajmal and Shah, Mubarak},

	    title     = {UniCon: Combating Label Noise Through Uniform Selection and Contrastive Learning},

	    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},

	    month     = {June},

	    year      = {2022},

	    pages     = {9676-9686}

	}