MixMatch
This is an unofficial PyTorch implementation of MixMatch: A Holistic Approach to Semi-Supervised Learning. The official Tensorflow implementation is here.
Now only experiments on CIFAR-10 are available.
This repository carefully implemented important details of the official implementation to reproduce the results.
Requirements
- Python 3.6+
- PyTorch 1.0
- torchvision 0.2.2 (older versions are not compatible with this code)
- tensorboardX
- progress
- matplotlib
- numpy
Usage
Train
Train the model by 250 labeled data of CIFAR-10 dataset:
python train.py --gpu <gpu_id> --n-labeled 250 --out cifar10@250
Train the model by 4000 labeled data of CIFAR-10 dataset:
python train.py --gpu <gpu_id> --n-labeled 4000 --out cifar10@4000
Monitoring training progress
tensorboard.sh --port 6006 --logdir cifar10@250
Results (Accuracy)
| #Labels | 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 | 88.71 | 88.96 | 90.52 | 92.23 | 93.52 |
(Results of this code were evaluated on 1 run. Results of 5 runs with different seeds will be updated later. )
References
@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}
}