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Pytorch implementation of the paper "Debiased Sample Selection for Combating Noisy Labels"

Primary LanguagePython

ITEM

Pytorch implementation of the paper "Debiased Sample Selection for Combating Noisy Labels"

Training

Hyperparameter setup

Our framework mainly contains two hyperparameters, i.e., the number of experts $m$ and the slope parameter $\beta$ in mapping function

We set $m=4$ and $\beta=3$ for all CIFAR experiments.

Run

For CIFAR-10/100 with symmetric or instance-dependent label noise

python Train_cifar.py --dataset ['cifar10', 'cifar100']
                      --batch_size 64
                      --noise_mode sym
                      --r 0.2
                      --cls_num 4
                      --beta 3
                      --gpuid 0

For CIAFAR-10N with worst, random 1/2/3.

python Train_cifarN.py --noise_mode ['worse_label', 'random_label1', 'random_label2', 'random_label3']
                       --cls_num 4
                       --beta 3
                       --gpuid 0