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Code for ICML2020 paper [“Normalized Loss Functions for Deep Learning with Noisy Labels"] https://arxiv.org/abs/2006.13554

License: MIT License

Python 91.13% Shell 8.87%

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Normalized Loss Functions - Active Passive Losses

Code for ICML2020 Paper "Normalized Loss Functions for Deep Learning with Noisy Labels"

Requirements

Python >= 3.6, PyTorch >= 1.3.1, torchvision >= 0.4.1, mlconfig

How To Run

Configs for the experiment settings

Check '*.yaml' file in the config folder for each experiment.

Arguments

noise_rate: noise rate
asym: use if it is asymmetric noise, default is symmetric
config_path: path to the configs folder
version: the config file name
exp_name: name of the experiments (as note)
seed: random seed

Example for 0.4 Symmetric noise rate with NCE+RCE loss

# CIFAR-10
$  python3  main.py --exp_name      test_exp            \
                    --noise_rate    0.4                 \
                    --version       nce+rce             \
                    --config_path   configs/cifar10/sym \
                    --seed          123

# CIFAR-100
$  python3  main.py --exp_name      test_exp             \
                    --noise_rate    0.4                  \
                    --version       nce+rce              \
                    --config_path   configs/cifar100/sym \
                    --seed          123

Citing this work

If you use this code in your work, please cite the accompanying paper:

@inproceedings{ma2020normalized,
  title={Normalized Loss Functions for Deep Learning with Noisy Labels},
  author={Ma, Xingjun and Huang, Hanxun and Wang, Yisen and Romano, Simone and Erfani, Sarah and Bailey, James},
  booktitle={ICML},
  year={2020}
}

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