Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning (2019)

Paper: https://arxiv.org/abs/1908.02983

Official Pytorch implementation from authors. Our pseudo-labeling approach achieves state-of-the-art performance for semi-supervised learning (SSL) in Image Classification.

Installation

Docker

• Install Docker and NVIDIA-Docker

• Build the image: docker build -t pseudolabeling .

• Start the container:

docker run --gpus all \
--name pseudolabeling \
-v $(pwd):/pseudolabeling \
-it -w="/pseudolabeling" \
pseudolabeling bash

To run the code without GPU, NVIDIA-Docker is not required and remove --gpus all

Manual install

Dependencies
python==3.5.2
pytorch==0.4.1
cuda==8.0
torchvision==0.2.1
torchcontrib==0.0.2
matplotlib==3.0.1
scikit-learn==0.20.0
tqdm==4.28.1
numpy==1.15.3

Usage

You can find an example script to run the poroposed SSL approach on CIFAR-10 with 500 labeled samples in RunScripts_SOTA500.sh, for CIFAR-100 with 4000 labeled samples in RunScripts_SOTA4000.sh, and for MiniImagenNet with 4000 labeled samples in RunScripts_SOTA4000.sh. Execute the script from the corresponding folder to train the model.

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

The most relevant parameters are the following:

• --labeled_samples: Number of labeled samples
• --epoch: Number of epochs of training
• --M: Epochs where the learning rate is divided by 10
• --network: Network architecture ("MT_Net", "WRN28_2_wn", "PreactResNet18_WNdrop", or "resnet18_wndrop")
• --DA: Type of data augmentation ("standard" or "jitter")

To run the CIFAR experiments download the corresponding dataset in the folder ./CIFAR10/data or ./CIFAR100/data. To run the MiniImageNet experiments download the ImageNet dataset, pre-process it (see create_dataset.txt), and place it in ./miniImagenet/data.

Test Errors

Number of labeled samples	500	1000	4000	10000
CIFAR-10	8.80 ± 0.45	6.85 ± 0.15	5.97 ± 0.15	----
CIFAR-100	----	----	37.55 ± 1.09	32.15 ± 0.5
MiniImageNet	----	----	56.49 ± 0.51	46.08 ± 0.11

Acknowledgements

We would like to thank [1] (https://github.com/benathi/fastswa-semi-sup) for the "13-layer" network implmentation, [2] (https://github.com/vikasverma1077/ICT) for the "WR_28_2" network implmentation, and [3] (https://github.com/CuriousAI/mean-teacher) for the data sampler code that we use in our code.

[1] Athiwaratkun, Ben and Finzi, Marc and Izmailov, Pavel and Wilson, Andrew Gordon, "There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average", in International Conference on Learning Representations (ICLR), 2019

[2] Verma, Vikas and Lamb, Alex and Kannala, Juho and Bengio, Yoshua and Lopez-Paz, David, "Interpolation Consistency Training for Semi-Supervised Learning", in International Joint Conferences on Artificial Intelligence (IJCAI), 2019.

[3] Antti Tarvainen, Harri Valpola, "Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results", in Advances in neural information processing systems, 2017.

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

 @inproceedings{pseudoLabel2019,
  title = {Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning},
  author = {Eric Arazo and Diego Ortego and Paul Albert and Noel E O'Connor and Kevin McGuinness},
  booktitle={2020 International Joint Conference on Neural Networks (IJCNN)},
  year={2020},
  organization={IEEE}
 } 

Eric Arazo, Diego Ortego, Paul Albert, Noel E. O'Connor, Kevin McGuinness, Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning, International Joint Conference on Neural Networks (IJCNN), 2020