semi-memory

Tensorflow Implementation of the paper Chen et al. Semi-Supervised Deep Learning with Memory, ECCV2018.

Getting Started

Prerequisite:

• Python 2.7.
• Tensorflow version >= 1.4.0.

Data preparation:

1. Download and prepare datasets:

bash scripts/download_prepare_datasets.sh

2. Convert image data to tfrecords:

bash scripts/convert_images_to_tfrecords.sh

Running Experiments

Training & Testing:

For example, to train and test on svhn, run the following command.

bash scripts/train_svhn_semi.sh

Citation

Please refer to the following if this repository is useful for your research.

Bibtex:

@inproceedings{chen2018semi,
  title={Semi-Supervised Deep Learning with Memory},
  author={Chen, Yanbei and Zhu, Xiatian and Gong, Shaogang},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2018}
}

License

This project is licensed under the MIT License - see the LICENSE.md file for details.