nazmul-karim170 / UNICON-Noisy-Label

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

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.

Paper

Code for Training

UNICON Framework

Example Run

After creating a virtual environment, run

pip install -r requirements.txt

Example run (CIFAR10 with 50% symmetric noise)

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)

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)

python Train_TinyImageNet.py --ratio 0.5

Example run (Clothing1M)

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

Example run (Webvision)

python Train_webvision.py 

Dataset

For datasets other than CIFAR10 and CIFAR100, you need to download them from their corresponding website.

Reference

If you have any questions, do not hesitate to contact [e-mail]

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}
}