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| # ELR+ | |
| This is an official PyTorch implementation of ELR+ method proposed in [Early-Learning Regularization Prevents Memorization of Noisy Labels](https://arxiv.org/abs/2007.00151). | |
| ## 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 | |
| ``` | |
| 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. | |
| ## References | |
| - S. Liu, J. Niles-Weed, N. Razavian and C. Fernandez-Granda "Early-Learning Regularization Prevents Memorization of Noisy Labels", 2020 | |