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ICLR‘2021: Robust Early-learning: Hindering the Memorization of Noisy Labels (PyTorch implementation).

This is the code for the paper: Robust Early-learning: Hindering the Memorization of Noisy Labels
Xiaobo Xia, Tongliang Liu, Bo Han, Chen Gong, Nannan Wang, Zongyuan Ge, Yi Chang.

Dependencies

We implement our methods by PyTorch on NVIDIA Tesla V100 GPU. The environment is as bellow:

• Ubuntu 16.04 Desktop
• PyTorch, version = 1.2.0
• CUDA, version = 10.0
• Anaconda3

Install requirements.txt

pip install -r requirements.txt

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.
Here is a training example:

python main.py \
    --dataset mnist \
    --noise_type symmetric \
    --noise_rate 0.2 \
    --seed 1

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

@inproceedings{xia2021robust,
  title={Robust early-learning: Hindering the memorization of noisy labels},
  author={Xia, Xiaobo and Liu, Tongliang and Han, Bo and Gong, Chen and Wang, Nannan and Ge, Zongyuan and Chang, Yi},
  booktitle={ICLR},
  year={2021}
}