Towards continual learning with the artificial neural twin applied to recycling processes
Mendez R, Maier A, Emmert J (2025)
Publication Type: Conference contribution
Publication year: 2025
Publisher: KIT Scientific Publishing
Pages Range: 295-307
Conference Proceedings Title: Optical Characterization of Materials
Event location: Karlsruhe, DEU
ISBN: 9783731514084
Abstract
With the increasing use of AI models in industrial processes, comes the need for more training data. Further, material packaging industry is an ever evolving sector, which makes the task of the AI models used by post-consumer package sorting facilities equally dynamic, rapidly outdating the training data, and requiring the generation of new expensive datasets. We propose to apply continual learning in combination with the Artificial Neural Twin (ANT), to continually train models without generating new data manually. We initially train with a small dataset, then, apply Orthogonal Weight Modification with training stimuli from quality control measurements collected by the ANT, and poof through experiments that this can replace the expensive process of dataset generation.
Involved external institutions
Germany (DE)How to cite
APA:
Mendez, R., Maier, A., & Emmert, J. (2025). Towards continual learning with the artificial neural twin applied to recycling processes. In Jürgen Beyerer, Thomas Längle, Jürgen Beyerer, Michael Heizmann (Eds.), Optical Characterization of Materials (pp. 295-307). Karlsruhe, DEU: KIT Scientific Publishing.
MLA:
Mendez, Ronald, Andreas Maier, and Johannes Emmert. "Towards continual learning with the artificial neural twin applied to recycling processes." Proceedings of the 7th International Conference on Optical Characterization of Materials, OCM 2025, Karlsruhe, DEU Ed. Jürgen Beyerer, Thomas Längle, Jürgen Beyerer, Michael Heizmann, KIT Scientific Publishing, 2025. 295-307.
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