Skip to content

Latest commit

 

History

History
83 lines (62 loc) · 3.55 KB

README.md

File metadata and controls

83 lines (62 loc) · 3.55 KB
Raw

Official PyTorch implementation for the ICML 2019 paper: "Unsupervised Label Noise Modeling and Loss Correction" https://arxiv.org/abs/1904.11238

Training images with correct (green) and incorrect (red) label Network predicitons post training
couldn't find image couldn't find image

You can find in RunScripts.sh an example script to run the code for 80% label noise (M-DYR-H and M-DYR-S) and 90% label noise (MD-DYR-SH).

Feel free to modify input parameters for any level of label noise (and other parameters in the paper).

Additionally, the code is now supporting both CIFAR-10 and CIFAR-100 but feel free to adapt it for other datasets such as TinyImagenet by changing the data loaders and modifying the noise addition function in utils.py

Dependencies
python == 3.6
pytorch == 0.4.1
cuda92
torchvision
matplotlib
scikit-learn
tqdm

Environement

If you are using conda, you can execute:

$ conda env create -f environment.yml
$ conda activate lnoise

This will include all dependencies in a new conda environement called lnoise

Supported datasets:

CIFAR-10 & CIFAR-100 datasets are currently supported and will be downloaded automatically to the path set with --dataset option

We run our approach on:

CPU: Intel(R) Core(TM) i7-6850K CPU @ 3.60GHz GPU: NVIDIA GTX1080Ti

Parameters details

Execute the following to get details about parameters. Most of them are set by default to replicate our experiments.

$ python train.py --h

Accuracies on CIFAR10

Algorithm\Noise level 0 20 50 80 90
M-DYR-H best 93.6 94.0 92.0 86.8 40.8
last 93.4 93.8 91.9 86.6 9.9
MD-DYR-SH best 93.6 93.8 90.6 82.4 69.1
last 92.7 93.6 90.3 77.8 68.7

Accuracies on CIFAR100

Algorithm\Noise level 0 20 50 80 90
M-DYR-H best 70.3 68.7 61.7 48.2 12.5
last 66.2 68.5 58.8 47.6 8.6
MD-DYR-SH best 73.3 73.9 66.1 41.6 24.3
last 71.3 73.34 65.4 35.4 20.5

Accuracies are reported at the end of 300 epochs of training.

Note: We thank authors from 1 for the mixup and Pytorch implementation of PreAct ResNet (https://github.com/facebookresearch/mixup-cifar10)
that we use in our code.

[1] Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz, "mixup: Beyond Empirical Risk Minimization", in International Conference on Learning Representations (ICLR), 2018.

Please consider citing the following paper if you find this work useful for your research.

 @inproceedings{ICML2019_UnsupervisedLabelNoise,
  title = {Unsupervised Label Noise Modeling and Loss Correction},
  authors = {Eric Arazo and Diego Ortego and Paul Albert and Noel E O'Connor and Kevin McGuinness},
  booktitle = {International Conference on Machine Learning (ICML)},
  month = {June},
  year = {2019}
 }

Eric Arazo*, Diego Ortego*, Paul Albert, Noel E. O'Connor, Kevin McGuinness, Unsupervised Label Noise Modeling and Loss Correction, International Conference on Machine Learning (ICML), 2019

*Equal contribution