This is an unofficial implementation of MixMatch: A Holistic Approach to Semi-Supervised Learning (NIPS 2019). Official code is here written by Google Research with Tensorflow. Paper summary is here written by myself.
You can easily install all requirements by the command
pip install -r requirements.txt
The biggest difference from the previous implementations is that it uses Albumentations for image augmentation instead of torchvision transforms. This can also be tuned by train configurations.
The code supports CIFAR-10, CIFAR-100, SVHN and STL-10 as mentioned in MixMatch paper.
Train the model by 250 labeled data of CIFAR-10 dataset (default).
python main.py --num-labels 250 --datasets CIFAR10
The code includes different hyperparameters for config including
- alpha (default=0.75): Parameter for beta distribution during MixUp stage
- lambda_u (default=75): Weight between supervised and unsupervised loss
- T (default=0.5): Temperature during softmax sharpening for entropy minimization
- K (default=2): Number of augmentations for unsupervised images
Default all follows from the paper.
| CIFAR-10 | 250 | 500 | 1000 | 2000 | 4000 |
|---|---|---|---|---|---|
| Paper | 88.92±0.87 | 90.35±0.94 | 92.25±0.32 | 92.97±0.15 | 93.76±0.06 |
| This code | #TODO | #TODO | #TODO | #TODO | #TODO |
- MixMatch-pytorch by YU1ut
- Mixmatch-pytorch-SSL by Jeffkang-94
- mixmatch by google-research
@article{berthelot2019mixmatch,
title={MixMatch: A Holistic Approach to Semi-Supervised Learning},
author={Berthelot, David and Carlini, Nicholas and Goodfellow, Ian and Papernot, Nicolas and Oliver, Avital and Raffel, Colin},
journal={arXiv preprint arXiv:1905.02249},
year={2019}
}