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:
- Download and prepare datasets:
bash scripts/download_prepare_datasets.sh
- 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.