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Demir, Emre und Traoré, Kalifou René und Camero, Andres (2024) Leveraging performance-based metadata for designing multi-objective NAS strategies for efficient models in Earth Observation. In: ESANN 2024 Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Seiten 209-214. www.i6doc.com/en/. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2024-10-09 - 2024-10-11, Brugge, Belgium. doi: 10.14428/esann/2024.ES2024-94. ISBN 978-2-87587-090-2.
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Offizielle URL: https://i6doc.com/en/info/?id=6
Kurzfassung
Earth Observational (EO) datasets present challenges that differ from traditional Computer Vision benchmarks often examined by the AutoML community. To assist EO researchers in leveraging AutoML techniques, we offer a NAS benchmark with performance meta-data specifically for an EO context. This dataset not only focuses on resource-efficient models crucial to EO but also includes hardware-based metrics. Moreover, we investigate performance prediction to build a data-centric approach for initializing multi-objective NAS search algorithms.
| elib-URL des Eintrags: | https://elib.dlr.de/207631/ | ||||||||||||||||
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| Dokumentart: | Konferenzbeitrag (Poster) | ||||||||||||||||
| Titel: | Leveraging performance-based metadata for designing multi-objective NAS strategies for efficient models in Earth Observation | ||||||||||||||||
| Autoren: |
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| Datum: | 2024 | ||||||||||||||||
| Erschienen in: | ESANN 2024 Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning | ||||||||||||||||
| Referierte Publikation: | Ja | ||||||||||||||||
| Open Access: | Ja | ||||||||||||||||
| Gold Open Access: | Nein | ||||||||||||||||
| In SCOPUS: | Nein | ||||||||||||||||
| In ISI Web of Science: | Nein | ||||||||||||||||
| DOI: | 10.14428/esann/2024.ES2024-94 | ||||||||||||||||
| Seitenbereich: | Seiten 209-214 | ||||||||||||||||
| Verlag: | www.i6doc.com/en/ | ||||||||||||||||
| ISBN: | 978-2-87587-090-2 | ||||||||||||||||
| Status: | veröffentlicht | ||||||||||||||||
| Stichwörter: | AutoML. Neural Architecture Search, Multi-objective, Benchmark, Earth Observation | ||||||||||||||||
| Veranstaltungstitel: | European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning | ||||||||||||||||
| Veranstaltungsort: | Brugge, Belgium | ||||||||||||||||
| Veranstaltungsart: | internationale Konferenz | ||||||||||||||||
| Veranstaltungsbeginn: | 9 Oktober 2024 | ||||||||||||||||
| Veranstaltungsende: | 11 Oktober 2024 | ||||||||||||||||
| HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||
| HGF - Programm: | Raumfahrt | ||||||||||||||||
| HGF - Programmthema: | Erdbeobachtung | ||||||||||||||||
| DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||
| DLR - Forschungsgebiet: | R EO - Erdbeobachtung | ||||||||||||||||
| DLR - Teilgebiet (Projekt, Vorhaben): | R - Künstliche Intelligenz, R - Optische Fernerkundung | ||||||||||||||||
| Standort: | Oberpfaffenhofen | ||||||||||||||||
| Institute & Einrichtungen: | Institut für Methodik der Fernerkundung > EO Data Science | ||||||||||||||||
| Hinterlegt von: | Traoré, Mr René | ||||||||||||||||
| Hinterlegt am: | 23 Okt 2024 09:25 | ||||||||||||||||
| Letzte Änderung: | 11 Nov 2024 08:52 |
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