Auflistung nach Schlagwort "domain adaptation"

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  • Damage localisation using disparate damage states via domain adaptation 
    Wickramarachchi, Chandula T.; Gardner, Paul; Poole, Jack; Hübler, Clemens; Jonscher, Clemens; Rolfes, Raimund (Cambridge : Cambridge University Press, 2024)
    A significant challenge of structural health monitoring (SHM) is the lack of labeled data collected from damage states. Consequently, the collected data can be incomplete, making it difficult to undertake machine learning ...
  • Deep domain adaptation by weighted entropy minimization for the classification of aerial images 
    Wittich, Dennis (Katlenburg-Lindau : Copernicus Publications, 2020)
    Fully convolutional neural networks (FCN) are successfully used for the automated pixel-wise classification of aerial images and possibly additional data. However, they require many labelled training samples to perform ...
  • Domain adaptation with cyclegan for change detection in the amazon forest 
    Soto, P.J.; Costa, G.A.O.P.; Feitosa, R.Q.; Happ, P.N.; Ortega, M.X.; Noa, J.; Almeida, C.A.; Heipke, Christian (Katlenburg-Lindau : Copernicus Publications, 2020)
    Deep learning classification models require large amounts of labeled training data to perform properly, but the production of reference data for most Earth observation applications is a labor intensive, costly process. In ...
  • Exploration and adaptation of large language models for specialized domains 
    van Aken, Betty (Hannover : Institutionelles Repositorium der Leibniz Universität Hannover, 2023)
    Large language models have transformed the field of natural language processing (NLP). Their improved performance on various NLP benchmarks makes them a promising tool—also for the application in specialized domains. Such ...
  • Iterative re-weighted instance transfer for domain adaptation 
    Paul, Andreas; Rottensteiner, Franz; Heipke, Christian (Göttingen : Copernicus GmbH, 2016)
    Domain adaptation techniques in transfer learning try to reduce the amount of training data required for classification by adapting a classifier trained on samples from a source domain to a new data set (target domain) ...
  • Using semantically paired images to improve domain adaptation for the semantic segmentation of aerial images 
    Gritzner, D.; Ostermann, J. (Katlenburg-Lindau : Copernicus Publications, 2020)
    Modern machine learning, especially deep learning, which is used in a variety of applications, requires a lot of labelled data for model training. Having an insufficient amount of training examples leads to models which ...