Sample Prior Guided Robust Model Learning to Suppress Noisy Labels

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Deep Learning PGDF
Overview

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 has not yet been submitted, we only release part of the experimental code. We will release all the experimental code after this paper is accepted by a conference.

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Comments
  • Requirements

    Requirements

    An installation file with the requirements would be handy to install all the dependencies with pip.

    I believe that there is a simple command to generate a requirements.txt given an environment.

    Thanks!

    opened by aldakata 3
  • details about mini webvision1.0 dataset

    details about mini webvision1.0 dataset

    Thanks for your brilliant work first. Actually we try to replicate your results, so i was wondering that how many samples does the mini Webvision 1.0 dataset your used in this work contain? Is that 61K? I was so confused because some works state that they also use a subset of the first 50 classes in webvision1.0 which contains more than 100K samples in training set, such as Aum[https://arxiv.org/abs/2001.10528]. Looking forward for your kindly help

    opened by Christophe-Jia 2
  • GMM after epoch 200

    GMM after epoch 200

    After reading the paper:

    Specifically, as shown in Figure 2, at each epoch, we get the clean probability wit of each sample from training loss by using Gaussian Mixture Model (GMM)

    But on the code this GMM is only accounted for after epoch 200. Is this an error or am I missing something? https://github.com/bupt-ai-cz/PGDF/blob/f7b6de71c0959c8a268a74da126a4e1858aeb290/train_cifar.py#L471

    Thanks in advance!

    opened by aldakata 1
Owner
CVSM Group - email: [email protected]
Codes of our papers are released in this GITHUB account.
CVSM Group - email: czhu@bupt.edu.cn
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