Approaches towards Resource-saving and Explainability/Transparency of Deep-learning-based Image Classification in Industrial Applications

Constantin Rieder, Markus Germann, Samuel Mezger, Klaus Scherer

2021

Abstract

In the present work a new approach for the concept-neutral access to information (in particular visual kind) is compiled. In contrast to language-neutral access, concept-neutral access does not require the need to know precise names or IDs of components. Language-neutral systems usually work with language-neutral metadata, such as IDs (unique terms) for components. Access to information is therefore significantly facilitated for the user in term-neutral access without required knowledge of such IDs. The AI models responsible for recognition transparently visualize the decisions and they evaluate the recognition with quality criteria to be developed (confidence). To the applicants’ knowledge, this has not yet been used in an industrial setting. The use of performant models in a mobile, low-energy environment is also novel and not yet established in an industrial setting.

Paper Citation

in Harvard Style

Rieder C., Germann M., Mezger S. and Scherer K. (2021). Approaches towards Resource-saving and Explainability/Transparency of Deep-learning-based Image Classification in Industrial Applications. In Proceedings of the 2nd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA, ISBN 978-989-758-526-5, pages 164-169. DOI: 10.5220/0010575901640169

in Bibtex Style

@conference{delta21,
author={Constantin Rieder and Markus Germann and Samuel Mezger and Klaus Scherer},
title={Approaches towards Resource-saving and Explainability/Transparency of Deep-learning-based Image Classification in Industrial Applications},
booktitle={Proceedings of the 2nd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,},
year={2021},
pages={164-169},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010575901640169},
isbn={978-989-758-526-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,
TI - Approaches towards Resource-saving and Explainability/Transparency of Deep-learning-based Image Classification in Industrial Applications
SN - 978-989-758-526-5
AU - Rieder C.
AU - Germann M.
AU - Mezger S.
AU - Scherer K.
PY - 2021
SP - 164
EP - 169
DO - 10.5220/0010575901640169