STOCO

Code release for ``Stochastic Consensus: Enhancing Semi-Supervised Learning with Consistency of Stochastic Classifiers'' accepted by ECCV 2022.

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Requirements

• python 3.6.4
• pytorch 1.4.0
• torchvision 0.5.0

Data preparation

The references of the used datasets are included in the paper.

Model training

1. Install necessary python packages.
2. Replace root and dataset in run.sh with those in one's own system.
3. Run command sh run.sh.

The results are saved in the folder ./results/.

Paper citation

@InProceedings{STOCO,
author={Tang, Hui
and Sun, Lin
and Jia, Kui},
title={Stochastic Consensus: Enhancing Semi-Supervised Learning with Consistency of Stochastic Classifiers},
booktitle={Computer Vision -- ECCV 2022},
year={2022},
pages={330-346},
}