Vinit Hegiste, M.Sc. B.Eng.

Organizational unit Innovative Factory Systems

Contact

Vinit.Hegiste@dfki.de

Address (Kaiserslautern)

Publications

All publications

Mapping of Newcomer Clients in Federated Learning Based on Activation Strength
Tatjana Legler; Vinit Hegiste; Martin Ruskowski
In: Francisco J. G. Silva; António B. Pereira; Raul D. S. G. Campilho (Hrsg.). Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems. International Conference on Flexible Automation and Intelligent Manufacturing (FAIM), Cham, Pages 1139-1148, ISBN 978-3-031-38241-3, Springer Nature Switzerland, 2024.
Enhancing Object Detection Performance for Small Objects Through Synthetic Data Generation and Proportional Class-Balancing Technique: A Comparative Study in Industrial Scenarios
Jibinraj Antony; Vinit Hegiste; Ali Nazeri; Hooman Tavakoli Ghinani; Snehal Walunj; Christiane Plociennik; Martin Ruskowski
In: Achim Wagner; Kosmas Alexopoulos; Sotiris Makris (Hrsg.). Advances in Artificial Intelligence in Manufacturing. European Symposium on Artificial Intelligence in Manufacturing (ESAIM-2023), Cham, Pages 90-105, ISBN 978-3-031-57496-2, Springer Nature Switzerland, 2024.
Enhancing Object Detection with Hybrid dataset in Manufacturing Environments: Comparing Federated Learning to Conventional Techniques
Vinit Hegiste; Snehal Walunj; Jibinraj Antony; Tatjana Legler; Martin Ruskowski
In: 2024 1st International Conference on Innovative Engineering Sciences and Technological Research (ICIESTR). International Conference on Innovative Engineering Sciences and Technological Research (ICIESTR-2024), May 14-15, Muscat, Oman, Pages 1-6, ISBN 979-8-3503-4863-7, IEEE, 2024.

Vinit Hegiste, M.Sc. B.Eng.

Mapping of Newcomer Clients in Federated Learning Based on Activation Strength

Enhancing Object Detection Performance for Small Objects Through Synthetic Data Generation and Proportional Class-Balancing Technique: A Comparative Study in Industrial Scenarios

Enhancing Object Detection with Hybrid dataset in Manufacturing Environments: Comparing Federated Learning to Conventional Techniques