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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 --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.
## Results
### CIFAR10
<center>
| 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% |
### CIAFAR100
| 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% |
</center>
## References
- S. Liu, J. Niles-Weed, N. Razavian and C. Fernandez-Granda "Early-Learning Regularization Prevents Memorization of Noisy Labels", 2020
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