DivideFlex

A simple method for 1st Learning and Mining with Noisy Labels Challenge IJCAI-ECAI 2022

Dependencies

You can use requirements.txt for building dependencies.

You can run run.sh with 5 pre-fixed seeds. Each run will be evaluated w.r.t. a random selected subset of CIFAR-10 test data with replacement, and take the average performance of 5 runs.
run learning.py

python learning.py --load_path last_model.pth --noise_type [noise_type]

Name		Name	Last commit message	Last commit date
Latest commit History 7 Commits
configs		configs
data		data
data_statistics		data_statistics
datasets		datasets
flexmatch		flexmatch
models		models
.gitignore		.gitignore
LICENSE.md		LICENSE.md
README.md		README.md
custom_writer.py		custom_writer.py
divideflex.py		divideflex.py
learning.py		learning.py
requirements.txt		requirements.txt
run.sh		run.sh
side_info_cifar100N.csv		side_info_cifar100N.csv
side_info_cifar10N.csv		side_info_cifar10N.csv
train_utils.py		train_utils.py
utils.py		utils.py