This repository contains code for the paper "Learning by Association - A versatile semi-supervised training method for neural networks." (Link).
It is implemented with TensorFlow. Please refer to the TensorFlow documentation for further information.
The core functions are implemented in semisup/backend.py.
The files train.py and eval.py demonstrate how to use them. A quick example is contained in mnist_train_eval.py.
In order to reproduce the results from the paper, please use the architectures and pipelines from the {stl10,svhn,synth}_tools.py. They are loaded automatically by setting the flag package in {train,eval}.py accordingly.
Before you get started, please make sure to add the following to your ~/.bashrc:
export PYTHONPATH=/path/to/learning_by_association:$PYTHONPATH
Copy the file semisup/data_dirs.py.template to semisup/data_dirs.py, adapt the paths and .gitignore this file.
If you use the code, please cite the paper "Learning by Association - A versatile semi-supervised training method for neural networks."
@string{cvpr="IEEE Conference on Computer Vision and Pattern Recognition (CVPR)"}
@InProceedings{haeusser-cvpr17,
author = "P. Haeusser and A. Mordvintsev and D. Cremers",
title = "Learning by Association - A versatile semi-supervised training method for neural networks",
booktitle = cvpr,
year = "2017",
titleurl = {haeusser_cvpr_17.pdf},
keywords = {semi-supervised, deep learning, neural networks, association},
}
For questions please contact Philip Haeusser ([email protected]) or Alexander Mordvintsev ([email protected]).