Robotics & Machine Learning Daily News2024,Issue(Mar.15) :116-117.

Findings on Machine Learning Detailed by Investigators at Lund University (Reusa bility Report: Unpaired Deep-learning Approaches for Holographic Image Reconstru ction)

Robotics & Machine Learning Daily News2024,Issue(Mar.15) :116-117.

Findings on Machine Learning Detailed by Investigators at Lund University (Reusa bility Report: Unpaired Deep-learning Approaches for Holographic Image Reconstru ction)

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Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Machine Learning. According to news reporting out of Lund, Sweden, by NewsRx editors, r esearch stated, “Deep-learning methods using unpaired datasets hold great potent ial for image reconstruction, especially in biomedical imaging where obtaining p aired datasets is often difficult due to practical concerns. A recent study by L ee et al. (Nature Machine Intelligence 2023) has introduced a parameterized phys ical model (referred to as FMGAN) using the unpaired approach for adaptive holog raphic imaging, which replaces the forward generator network with a physical mod el parameterized on the propagation distance of the probing light.”

Key words

Lund/Sweden/Europe/Emerging Technolog ies/Machine Intelligence/Machine Learning/Lund University

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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