TNLPAD

TPAMI: Tackling noisy labels with network parameter additive decomposition (PyTorch implementation).

This is the code for the paper: Tackling noisy labels with network parameter additive decomposition

Jingyi Wang, Xiaobo Xia, Long Lan, Xinghao Wu, Jun Yu, Wenjing Yang, Bo Han, Tongliang Liu.

Dependencies

Ubuntu 18.04

Python 3.6

PyTorch, verion=1.4.0

CUDA, version=10.1

Experiments

We verify the effectiveness of the proposed method on synthetic noisy datasets. In this repository, we provide the used datasets (the images and labels have been processed to .npy format). You should put the datasets in the folder “data” when you have downloaded them. Training example:

python main.py --dataset mnist --noise_type symmetric --noise_rate 0.4 --c2 2 --seed 1

If you find this code useful in your research, please cite:

@ARTICLE{wang2024tackling,
  author={Wang, Jingyi and Xia, Xiaobo and Lan, Long and Wu, Xinghao and Yu, Jun and Yang, Wenjing and Han, Bo and Liu, Tongliang},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Tackling Noisy Labels With Network Parameter Additive Decomposition}, 
  year={2024},
  volume={},
  number={},
  pages={1-14},
  keywords={Noise measurement;Training;Additives;Robustness;Training data;Noise robustness;Upper bound;Early stopping;learning with noisy labels;memorization effect;parameter decomposition},
  doi={10.1109/TPAMI.2024.3382138}
}