Niemeijer, Joshua und Schäfer, Jörg P. (2022) Domain Adaptation and Generalization: A Low-Complexity Approach. Conference on Robot Learning (CoRL) 2022, 2022-12-14 - 2022-12-18, Auckland, New Zealand. ISSN 26403498.
PDF
- Nur DLR-intern zugänglich
24MB |
Offizielle URL: https://proceedings.mlr.press/v205/niemeijer23a.html
Kurzfassung
Well-performing deep learning methods are essential in today's perception of robotic systems such as autonomous driving vehicles. Ongoing research is due to the real-life demands for robust deep learning models against numerous domain changes and cheap training processes to avoid costly manual-labeling efforts. These requirements are addressed by unsupervised domain adaptation methods, in particular for synthetic to real-world domain changes. Recent top-performing approaches are hybrids consisting of multiple adaptation technologies and complex training processes. In contrast, this work proposes EasyAdap, a simple and easy-to-use unsupervised domain adaptation method achieving near state-of-the-art performance on the synthetic to real-world domain change. Our evaluation consists of a comparison to numerous top-performing methods, and it shows the competitiveness and further potential of domain adaptation and domain generalization capabilities of our method. We contribute and focus on an extensive discussion revealing possible reasons for domain generalization capabilities, which is necessary to satisfy real-life application's demands.
elib-URL des Eintrags: | https://elib.dlr.de/192935/ | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Dokumentart: | Konferenzbeitrag (Vortrag) | ||||||||||||
Titel: | Domain Adaptation and Generalization: A Low-Complexity Approach | ||||||||||||
Autoren: |
| ||||||||||||
Datum: | Dezember 2022 | ||||||||||||
Referierte Publikation: | Ja | ||||||||||||
Open Access: | Nein | ||||||||||||
Gold Open Access: | Nein | ||||||||||||
In SCOPUS: | Nein | ||||||||||||
In ISI Web of Science: | Nein | ||||||||||||
ISSN: | 26403498 | ||||||||||||
Status: | veröffentlicht | ||||||||||||
Stichwörter: | unsupervised domain adaptation, semantic segmentation, domain generalization | ||||||||||||
Veranstaltungstitel: | Conference on Robot Learning (CoRL) 2022 | ||||||||||||
Veranstaltungsort: | Auckland, New Zealand | ||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||
Veranstaltungsbeginn: | 14 Dezember 2022 | ||||||||||||
Veranstaltungsende: | 18 Dezember 2022 | ||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||
HGF - Programm: | Verkehr | ||||||||||||
HGF - Programmthema: | Straßenverkehr | ||||||||||||
DLR - Schwerpunkt: | Verkehr | ||||||||||||
DLR - Forschungsgebiet: | V ST Straßenverkehr | ||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | V - KoKoVI - Koordinierter kooperativer Verkehr mit verteilter, lernender Intelligenz | ||||||||||||
Standort: | Berlin-Adlershof , Braunschweig | ||||||||||||
Institute & Einrichtungen: | Institut für Verkehrssystemtechnik Institut für Verkehrssystemtechnik > Kooperative Systeme, BS Institut für Verkehrssystemtechnik > Kooperative Systeme, BA | ||||||||||||
Hinterlegt von: | Niemeijer, Joshua | ||||||||||||
Hinterlegt am: | 10 Jan 2023 10:02 | ||||||||||||
Letzte Änderung: | 02 Sep 2024 09:26 |
Nur für Mitarbeiter des Archivs: Kontrollseite des Eintrags