This is the implementation of our ICML'22 paper (Revisiting Consistency Regularization for Deep Partial Label Learning).

Requirements: Python 3.6.9, numpy 1.19.5, torch 1.9.1, torchvision 0.10.1.

You need to:

1. Download SVHN and CIFAR-10 datasets into './data/'.
2. Run the following demos:

python main.py --dataset cifar10 --model widenet --data-dir ./data/cifar10/ --lam 1 --lr 0.1 --trial 1  --rate 0.7
python main.py --dataset cifar10 --model widenet --data-dir ./data/cifar10/ --lam 1 --lr 0.1 --trial 1  --rate=-1
python main.py --dataset cifar100 --model widenet --data-dir ./data/cifar100/ --lam 1 --lr 0.1 --trial 1  --rate 0.1

If you have any further questions, please feel free to send an e-mail to: dongdongwu@seu.edu.cn. Have fun!