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# johny-c / incremental-label-propagation

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

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# 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>} },
}


This work is released under the [MIT Licence].

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

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