This repo is the official implementation of our paper "Sample Prior Guided Robust Model Learning to Suppress Noisy Labels ".
If you use this code/data for your research, please cite our paper "Sample Prior Guided Robust Model Learning to Suppress Noisy Labels ".
@misc{chen2021sample,
title={Sample Prior Guided Robust Model Learning to Suppress Noisy Labels},
author={Wenkai Chen and Chuang Zhu and Yi Chen},
year={2021},
eprint={2112.01197},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Take CIFAR-10 with 50% symmetric noise as an example:
First, please modify the data_path
in presets.json
to indicate the location of your dataset.
Then, run
python train_cifar_getPrior.py --preset c10.50sym
to get the prior knowledge. Related files will be saved in checkpoints/c10/50sym/saved/
.
Next, run
python train_cifar.py --preset c10.50sym
for the subsequent training process.
c10
means CIFAR-10, 50sym
means 50% symmetric noise.
Similarly, if you want to take experiment on CIFAR-100 with 20% symmetric noise, you can use the command:
python train_cifar_getPrior.py --preset c100.20sym
python train_cifar.py --preset c100.20sym
The (basic) semi-supervised learning part of our code is borrow from the official DM-AugDesc implementation.
Since this paper is still being submitted, we only release part of the experimental code. We will release all the experimental code after being accepted.