Authors:
Constantin Rieder
;
Markus Germann
;
Samuel Mezger
and
Klaus Peter Scherer
Affiliation:
Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, Germany
Keyword(s):
Deep Learning, Information Systems, Intelligent Assistance, Transparency, Continuous Self Learning, Explainability, Neural Networks, Resource Saving Image Classification.
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.