This is the code for our submisssion "LaplaceConfidence: a Graph-based Approach for Learning with Noisy Labels"
PyTorch CIFAR Training
optional arguments:
-h, --help show this help message and exit
--batch_size BATCH_SIZE
train batchsize
--warm_up WARM_UP warm epochs
--lr LR, --learning_rate LR
learning rate
--num_epochs NUM_EPOCHS
number of trainig epochs
--gpuid GPUID
--seed SEED
--save_name SAVE_NAME
--data_path DATA_PATH
path to dataset
--dataset DATASET
--resume RESUME
--T T temperature for sharping pseudo-labels
--knn KNN knn number for constructing graph
--pca PCA PCA dimension
--r R noise ratio
--noise_mode NOISE_MODE choose symmetrical or asymmetrical noise
You first need to download the public dataset CIFAR in here, then run (the label noise will be generated automaticlly):
python3 GLR_ce.py --data_path ** --dataset ** --num_class ** --r ** --knn **
You first need to download the public dataset WebVision in here Facing a real-world noisy dataset, we don't need to preprocess the label information. Just run
python3 LC_cifar.py --data_path ** --save_name LC_loss
python==3.6.8
scikit-learn==0.23.2
torch==1.7.0+cu101
scipy==1.6.2
Pillow==8.2.0
pandas==1.2.4
numpy==1.22.4