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 (haeusser@cs.tum.edu) or Alexander Mordvintsev (moralex@google.com).