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Authors: Christian Witte 1 ; 2 ; René Schuster 3 ; Syed Bukhari 1 ; Patrick Trampert 1 ; Didier Stricker 2 ; 3 and Georg Schneider 1

Affiliations: 1 ZF Friedrichshafen AG, Saarbrücken, Germany ; 2 University of Kaiserlautern - TUK, Kaiserslautern, Germany ; 3 DFKI - German Research Center for Artificial Intelligence, Kaiserslautern, Germany

Keyword(s): Continual Learning, Catastrophic Forgetting, Object Detection, Autonomous Driving.

Abstract: Incorporating unseen data in pre-trained neural networks remains a challenging endeavor, as complete retraining is often impracticable. Yet, training the networks sequentially on data with different distributions can lead to performance degradation for previously learned data, known as catastrophic forgetting. The sequential training paradigm and the mitigation of catastrophic forgetting are subject to Continual Learning (CL). The phenomenon of forgetting poses a challenge for applications with changing distributions and prediction objectives, including Autonomous Driving (AD). Our work aims to illustrate the severity of catastrophic forgetting for object detection for class- and domain-incremental learning. We propose four hypotheses, as we investigate the impact of the ordering of sequential increments and the underlying data distribution of AD datasets. Further, the influence of different object detection architectures is examined. The results of our empirical study highlight the major effects of forgetting for class-incremental learning. Moreover, we show that domain-incremental learning suffers less from forgetting but is highly dependent on the design of the experiments and choice of architecture. (More)

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Paper citation in several formats:
Witte, C.; Schuster, R.; Bukhari, S.; Trampert, P.; Stricker, D. and Schneider, G. (2023). Severity of Catastrophic Forgetting in Object Detection for Autonomous Driving. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-626-2; ISSN 2184-4313, SciTePress, pages 262-269. DOI: 10.5220/0011634500003411

@conference{icpram23,
author={Christian Witte. and René Schuster. and Syed Bukhari. and Patrick Trampert. and Didier Stricker. and Georg Schneider.},
title={Severity of Catastrophic Forgetting in Object Detection for Autonomous Driving},
booktitle={Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2023},
pages={262-269},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011634500003411},
isbn={978-989-758-626-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Severity of Catastrophic Forgetting in Object Detection for Autonomous Driving
SN - 978-989-758-626-2
IS - 2184-4313
AU - Witte, C.
AU - Schuster, R.
AU - Bukhari, S.
AU - Trampert, P.
AU - Stricker, D.
AU - Schneider, G.
PY - 2023
SP - 262
EP - 269
DO - 10.5220/0011634500003411
PB - SciTePress