Co-Correcting

Official implementation of Co-Correcting: Noise-tolerant Medical Image Classification via collaborative Label Probability Estimation

Requirements:

• python3.6
• numpy
• torch-1.4.0
• torchvision-0.5.0

Usage

Co-Correcting.py is used for both training a model on dataset with noisy labels and validating it.

Here is an example:

python Co-Correcting.py --dir ./experiment/ --dataset 'mnist' --noise_type sn --noise 0.2 --forget-rate 0.2

or you can train Co-Correcting with .sh:

sh script/mnist.sh