Liu, Chenying und Albrecht, Conrad M und Wang, Yi und Zhu, Xiao Xiang (2022) Peaks Fusion assisted Early-stopping Strategy for Overhead Imagery Segmentation with Noisy Labels. IEEE Big Data 2022, 2022-12-17 - 2022-12-20, Osaka, Japan. doi: 10.1109/BigData55660.2022.10020164.
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Offizielle URL: https://sites.google.com/view/bigdata-adocs/program#h.v0qd0mij9wnd
Kurzfassung
Automatic label generation systems, which are capable to generate huge amounts of labels with limited human efforts, enjoy lots of potential in the deep learning era. These easy-to-come-by labels inevitably bear label noises due to a lack of human supervision and can bias model training to some inferior solutions. However, models can still learn some plausible features, before they start to overfit on noisy patterns. Inspired by this phenomenon, we propose a new Peaks fusion assisted EArly-Stopping (PEAS) approach for imagery segmentation with noisy labels, which is mainly composed of two parts. First, a fitting based early-stopping criterion is used to detect the turning phase from which models are about to mimic noise details. After that, a peaks fusion strategy is applied to select reliable models in the detection zone to generate final fusion results. Here, validation accuracies are utilized as indicators in model selection. The proposed method was evaluated on New York City dataset whose labels were automatically collected by a rule-based label generation system, thus noisy to some extent due to a lack of human supervision. The experimental results showed that the proposed PEAS method can achieve both promising statistical and visual results when trained with noisy labels.
elib-URL des Eintrags: | https://elib.dlr.de/190707/ | ||||||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Vortrag) | ||||||||||||||||||||
Titel: | Peaks Fusion assisted Early-stopping Strategy for Overhead Imagery Segmentation with Noisy Labels | ||||||||||||||||||||
Autoren: |
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Datum: | 2022 | ||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||
In SCOPUS: | Nein | ||||||||||||||||||||
In ISI Web of Science: | Nein | ||||||||||||||||||||
DOI: | 10.1109/BigData55660.2022.10020164 | ||||||||||||||||||||
Seitenbereich: | Seiten 1-6 | ||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||
Stichwörter: | deep learning, semantic segmentation, noisy labels, early stopping | ||||||||||||||||||||
Veranstaltungstitel: | IEEE Big Data 2022 | ||||||||||||||||||||
Veranstaltungsort: | Osaka, Japan | ||||||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||||||
Veranstaltungsbeginn: | 17 Dezember 2022 | ||||||||||||||||||||
Veranstaltungsende: | 20 Dezember 2022 | ||||||||||||||||||||
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 | ||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||
Institute & Einrichtungen: | Institut für Methodik der Fernerkundung > EO Data Science | ||||||||||||||||||||
Hinterlegt von: | Liu, Chenying | ||||||||||||||||||||
Hinterlegt am: | 25 Nov 2022 09:03 | ||||||||||||||||||||
Letzte Änderung: | 24 Apr 2024 20:51 |
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