ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks

Please kindly cite the following papers if you find this repo useful.

@inproceddings{wang2021proselflc,
  title={ {ProSelfLC}: Progressive Self Label Correction
  for Training Robust Deep Neural Networks},
  author={Wang, Xinshao and Hua, Yang and Kodirov, Elyor and Clifton, David A and Robertson, Neil M},
  booktitle={CVPR},
  year={2021}
}
@phdthesis{wang2020example,
  title={Example weighting for deep representation learning},
  author={Wang, Xinshao},
  year={2020},
  school={Queen's University Belfast}
}
@article{wang2019derivative,
  title={Derivative Manipulation for General Example Weighting},
  author={Wang, Xinshao and Kodirov, Elyor and Hua, Yang and Robertson, Neil},
  journal={arXiv preprint arXiv:1905.11233},
  year={2019}
}
@article{wang2019imae,
  title={{IMAE} for Noise-Robust Learning: Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude�s Variance Matters},
  author={Wang, Xinshao and Hua, Yang and Kodirov, Elyor and Robertson, Neil M},
  journal={arXiv preprint arXiv:1903.12141},
  year={2019}
}

Set the Pipenv From Scratch

• sudo apt update && sudo apt upgrade
• sudo apt install python3.8
• curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
• python3.8 get-pip.py
• vim ~/.bashrc -> add export PATH='/home/ubuntu/.local/bin:$PATH' -> source ~/.bashrc
• pip3 install pipenv

Build env for this repo using pipenv

• git clone git@github.com:XinshaoAmosWang/ProSelfLC-CVPR2021.git
• cd ProSelfLC-CVPR2021
• pipenv install -e .

• The results are well sinked and organised, e.g.,
  proselflc_experiments/cifar100_symmetric_noise_rate_0.2/shufflenetv2
• 
• 
• accuracy_loss.xlsx

• params.csv