This repository contains an implementation of an open-set recognition method using deep learning. The method was proposed in our "Open-Set Recognition Using Intra-Class Splitting" paper presented at the IEEE European Signal Processing Conference 2019. It is based on intra-class splitting, i.e. splitting given normal samples into typical and atypical subsets:

Open-Set Recognition Using Intra-Class Splitting
by Patrick Schlachter, Yiwen Liao and Bin Yang
Institute of Signal Processing and System Theory, University of Stuttgart, Germany
IEEE European Signal Processing Conference 2019 in A Coruña, Spain

If you use this work for your research, please cite our paper:

@inproceedings{schlachter2019osr,
	author={Patrick Schlachter, Yiwen Liao and Bin Yang},
	booktitle={2019 IEEE European Signal Processing Conference (EUSIPCO)},
	title={Open-Set Recognition Using Intra-Class Splitting},
	year={2019},
	month={September},
}

Contains build and train functions of the underlying neural network models.

Contains evaluation, visualization and util functions.

The main function to start training and evaluation.

Imports necessary Python packages.