pgdf-1's Introduction

PGDF

This repo is the official implementation of our paper "Sample Prior Guided Robust Model Learning to Suppress Noisy Labels ".

Citation

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}
}

Training

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

Additional Info

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.

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