Machine Learning for Advanced Solar Cell Production. Adversarial Denoising, Sub-pixel Alignment and the Digital Twin: Paper presented at Climate Change with Machine Learning Workshop at 34th Conference on Neural Information Processing Systems, NeurIPS 2020, December 6, 2020, Online, Vancouver, Canada (Unknown)
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- New search for: Demant, M.
- New search for: Kurumundayil, L.
- New search for: Kunze, P.
- New search for: Woernhoer, A.
- New search for: Kovvali, A.
- New search for: Rein, S.
- New search for: Demant, M.
- New search for: Kurumundayil, L.
- New search for: Kunze, P.
- New search for: Woernhoer, A.
- New search for: Kovvali, A.
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2020
- Miscellaneous / Electronic Resource
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Title:Machine Learning for Advanced Solar Cell Production. Adversarial Denoising, Sub-pixel Alignment and the Digital Twin: Paper presented at Climate Change with Machine Learning Workshop at 34th Conference on Neural Information Processing Systems, NeurIPS 2020, December 6, 2020, Online, Vancouver, Canada
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Contributors:Demant, M. ( author ) / Kurumundayil, L. ( author ) / Kunze, P. ( author ) / Woernhoer, A. ( author ) / Kovvali, A. ( author ) / Rein, S. ( author )
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Publisher:
- New search for: Fraunhofer Publica
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Publication date:2020
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Type of media:Miscellaneous
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Type of material:Electronic Resource
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Language:Unknown
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Keywords:artificial intelligence , Silicium-Photovoltaik , Photovoltaik , selective emitter , representation learning , Charakterisierung von Prozess- und Silicium-Materialien , solar cell , Metallisierung und Strukturierung , quality inspection , deep learning , generative adversarial networks , denoising , Messtechnik und Produktionskontrolle , optical characterization
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