Official Repo: https://github.com/YisenWang/symmetric_cross_entropy_for_noisy_labels
Reproduce result for ICCV2019 paper "Symmetric Cross Entropy for Robust Learning with Noisy Labels"
- --loss: 'SCE', 'CE'
- --nr: 0.0 to 1.0 specify the nosie rate.
- --dataset_type: 'cifar10' or 'cifar100'
- --alpha: alpha for SCE
- --beta: beta for SCE
- --seed: random seed
- --version: For experiment notes
Example for 0.4 Symmetric noise rate with SCE loss
# CIFAR10
$ python3 -u train.py --loss SCE \
--dataset_type cifar10 \
--l2_reg 1e-2 \
--seed 123 \
--alpha 0.1 \
--beta 1.0 \
--version SCE0.4_CIFAR10 \
--nr 0.4
# CIFAR100
$ python3 -u train.py --lr 0.01 \
--loss SCE \
--dataset_type cifar100 \
--l2_reg 1e-2 \
--seed 123 \
--alpha 6.0 \
--beta 1.0 \
--version SCE0.4_CIFAR100 \
--nr 0.4
Result of best Epoch
Loss | 0.0 | 0.2 | 0.4 | 0.6 | 0.8 |
---|---|---|---|---|---|
CE | 92.68 | 84.70 | 72.77 | 54.14 | 31.23 |
SCE | 92.05 | 89.96 | 84.65 | 73.77 | 36.28 |
Loss | 0.0 | 0.2 | 0.4 | 0.6 | 0.8 |
---|---|---|---|---|---|
CE | 73.84 | 61.70 | 42.88 | 20.47 | 4.88 |
SCE | 73.57 | 62.31 | 46.50 | 24.00 | 12.51 |