1904.11238

Training images with correct (green) and incorrect (red) label	Network predicitons post training

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

PaulAlbert31 / LabelNoiseCorrection