Gryshchuk, Vadym and Weber, Cornelius and Loo, Chu Kiong and Wermter, Stefan (2021) Lifelong learning from event-based data. In: ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 6-8 October 2021, Virtual, Online.
Full text not available from this repository.Abstract
Lifelong learning is a long-standing aim for artificial agents that act in dynamic environments, in which an agent needs to accumulate knowledge incrementally without forgetting previously learned representations. We investigate methods for learning from data produced by event cameras and compare techniques to mitigate forgetting while learning incrementally. We propose a model that is composed of both, feature extraction and continuous learning. Furthermore, we introduce a habituation-based method to mitigate forgetting. Our experimental results show that the combination of different techniques can help to avoid catastrophic forgetting while learning incrementally from the features provided by the extraction module. © 2021 ESANN Intelligence and Machine Learning. All rights reserved.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Funders: | Alexander von Humboldt-Stiftung, Deutsche Forschungsgemeinschaft [Grant No: TRR169] |
| Uncontrolled Keywords: | Machine learning, Artificial agents; Catastrophic forgetting; Continuous learning; Dynamic environments; Event-based; Features extraction; Life long learning, Extraction |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Artificial Intelligence |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 02 Jul 2024 04:38 |
| Last Modified: | 02 Jul 2024 04:38 |
| URI: | http://eprints.um.edu.my/id/eprint/36105 |
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