ProtoSemi

This is the official repository of paper Rethinking Noisy Label Learning in Real-world Annotation Scenarios.

Setup

This implemetation is based on Python3. To run the code, you need the following dependencies:

• torch==1.7.1

• torchvision==0.8.2

• tensorboard==2.11.2

• numpy

• scikit-learn

You can simply run

pip install -r requirements.txt

Repository structure

We select some important files for detailed description.

|-- code 
    |-- data_preprocess # read the CIFAR-N dataset
    |-- config.py # hyperparameters
    |-- main.py 
    |-- model.py
    |-- myssl.py # semi-superivised learning
    |-- myutils.py 
    |-- sample_splits_backup.py # old sample split
    |-- sample_splits # new sample split
|-- data
    |-- CIFAR-N

Run

1. You can run like the script in the following:

cd code
CUDA_VISIBLE_DEVICES=0 python -u main.py --dataset cifar100 --noise_type noisy100 --lr 0.02 --epochs 500 --weight_decay 5e-4 --sample_split proto --warmups 20 --ssl mixmatch  --cos_up_bound 0.99 --cos_low_bound 0.90 --proto_epochs 1

1. You can reproduce the experimental results of our method by running the script:

cd code
bash reproduce.sh

Attribution

Parts of this code are based on the following repositories:

• Dividemix

• PES

• CIFAR-N