ELR

This is an official PyTorch implementation of ELR method proposed in Early-Learning Regularization Prevents Memorization of Noisy Labels.

Usage

Train the network on the Symmmetric Noise CIFAR-10 dataset (noise rate = 0.8):

python train.py -c config_cifar10.json --percent 0.8

Train the network on the Asymmmetric Noise CIFAR-10 dataset (noise rate = 0.4):

python train.py -c config_cifar10_asym.json --percent 0.4 --asym 1

Train the network on the Asymmmetric Noise CIFAR-100 dataset (noise rate = 0.4):

python train.py -c config_cifar100.json --percent 0.4 --asym 1

The config files can be modified to adjust hyperparameters and optimization settings.

Method	20%	40%	60%	80%	40% Asym
ELR	91.16%	89.15%	86.12%	73.86%	90.12%
ELR (cosine annealing)	91.12%	91.43%	88.87%	80.69%	90.35%

Method	20%	40%	60%	80%	40% Asym
ELR	74.21%	68.28%	59.28%	29.78%	73.71%
ELR (cosine annealing)	74.68%	68.43%	60.05%	30.27%	73.96%

S. Liu, J. Niles-Weed, N. Razavian and C. Fernandez-Granda "Early-Learning Regularization Prevents Memorization of Noisy Labels", 2020