zhaoxin94 / UNICON-Noisy-Label

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

UNICON-Noisy-Label

Official Implementation of the CVPR 2022 paper "UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning" https://arxiv.org/pdf/2203.14542.pdf

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 proper folder. That is why they are needed to 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   

Dataset

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

Reference

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

Also, if you find our work useful please cite:

@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}
}