• learning-to-reweight-examples
• • Environment
  • • Installation
  • MNIST binary classification experiment
  • CIFAR noisy label experiments
  • • Download CIFAR dataset
    • CIFAR-10/100 uniform flip noise experiment
    • CIFAR-10/100 background flip noise experiment
  • Citation

learning-to-reweight-examples

Code for paper Learning to Reweight Examples for Robust Deep Learning. [arxiv]

Environment

We tested the code on

• tensorflow 1.10
• python 3

Other dependencies:

• numpy
• tqdm
• six
• protobuf

Installation

The following command makes the protobuf configurations.

make

MNIST binary classification experiment

python -m mnist.mnist_train --exp ours

Please see mnist/mnist_train.py for more options.

CIFAR noisy label experiments

Download CIFAR dataset

bash cifar/download_cifar.sh ./data

Config files are located in cifar/configs. For ResNet-32, use
cifar/configs/cifar-resnet-32.prototxt. For Wide ResNet-28, use
cifar/configs/cifar-wide-resnet-28-10.prototxt.

CIFAR-10/100 uniform flip noise experiment

python -m cifar.cifar_train --config [CONFIG]

Please see cifar/cifar_train.py for more options.

CIFAR-10/100 background flip noise experiment

python -m cifar.cifar_train_background --config [CONFIG]

Please see cifar/cifar_train_background.py for more options.

Citation

If you use our code, please consider cite the following: Mengye Ren, Wenyuan Zeng, Bin Yang, Raquel
Urtasun. Learning to Reweight Examples for Robust Deep Learning. ICML 2018.

@inproceedings{ren18l2rw,
  author    = {Mengye Ren and Wenyuan Zeng and Bin Yang and Raquel Urtasun},
  title     = {Learning to Reweight Examples for Robust Deep Learning},
  booktitle = {ICML},
  year      = {2018},
}