Open-Set Recognition Using Intra-Class Splitting

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:

Paper

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},
}

Repository

models

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

toolkits

Contains evaluation, visualization and util functions.

main.py

The main function to start training and evaluation.

packages.py

Imports necessary Python packages.