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README.md

Incremental Label Propagation

This repository provides the implementation of our paper "Incremental Semi-Supervised Learning from Streams for Object Classification" (Ioannis Chiotellis*, Franziska Zimmermann*, Daniel Cremers and Rudolph Triebel, IROS 2018). All results presented in our work were produced with this code.

Installation

The code was developed in python 3.5 under Ubuntu 16.04. You can clone the repo with:

git clone https://github.com/johny-c/incremental-label-propagation.git

Datasets

  • KITTI

The repository includes 64-dimentional features extracted from KITTI sequences compressed in a zip file (data/kitti_features.zip). The included files will be extracted automatically if one of the included experiments is run on KITTI.

  • MNIST

A script will automatically download the MNIST dataset if an experiment is run on it.

Experiments

The repository includes scripts that replicate the experiments found in the paper, including:

  • Varying the number of labeled points or the ratio of labeled points in the data.
  • Varying the number of labeled or unlabeled neighbors considered for each node.
  • Varying the hyperparameter $$\theta$$ that controls the propagation area size.

To run an experiment with varying $$\theta$$:

python ilp/experiments/var_theta.py -d mnist

You can set different experiment options in the .yaml files found in the experimens/cfg directory.

WARNING:

The included experiment scripts compute and store statistics after every new data point, therefore the resulting output files are very large.

Publication

If you use this code in your work, please cite the following paper.

Ioannis Chiotellis*, Franziska Zimmermann*, Daniel Cremers and Rudolph Triebel, "Incremental Semi-Supervised Learning from Streams for Object Classification", in proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018). (pdf)

*equal contribution

@InProceedings{chiotellis2018incremental,
  author = "I. Chiotellis and F. Zimmermann and D. Cremers and R. Triebel",
  title = "Incremental Semi-Supervised Learning from Streams for Object Classification",
  booktitle = iros,
  year = "2018",
  month = "October",
  keywords={stream-based learning, sequential data, semi-supervised learning, object classification},
  note = {{<a href="https://github.com/johny-c/incremental-label-propagation" target="_blank">[code]</a>} },
}

License and Contact

This work is released under the [MIT Licence].

Contact John Chiotellis ✉️ for questions, comments and reporting bugs.

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Incremental Label Propagation (ILP) - Incremental Semi-Supervised Learning from Streams for Object Classification

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