Zobrazeno 1 - 10
of 396
pro vyhledávání: '"conditional GAN"'
Autor:
Lukas Drees, Dereje T. Demie, Madhuri R. Paul, Johannes Leonhardt, Sabine J. Seidel, Thomas F. Döring, Ribana Roscher
Publikováno v:
Plant Methods, Vol 20, Iss 1, Pp 1-28 (2024)
Abstract Background Image-based crop growth modeling can substantially contribute to precision agriculture by revealing spatial crop development over time, which allows an early and location-specific estimation of relevant future plant traits, such a
Externí odkaz:
https://doaj.org/article/c39b90d45ab048c9b17d50f4db0b12c1
Publikováno v:
IEEE Access, Vol 12, Pp 119647-119659 (2024)
Conditional generative adversarial networks (cGANs) have demonstrated remarkable success due to their class-wise controllability and superior quality for complex generation tasks. Typical cGANs solve the joint distribution matching problem by decompo
Externí odkaz:
https://doaj.org/article/e046fd8749694c7b9dbc416b0d4c1dd4
Publikováno v:
Energies, Vol 17, Iss 23, p 5877 (2024)
This paper presents a novel framework that integrates Conditional Generative Adversarial Networks (CGANs) and TimeGAN to generate synthetic Building-Integrated Photovoltaic (BIPV) power data, addressing the challenge of data scarcity in this domain.
Externí odkaz:
https://doaj.org/article/9c234d9970d6472ab415d67453a6a042
Publikováno v:
Discover Artificial Intelligence, Vol 3, Iss 1, Pp 1-14 (2023)
Abstract Medical image classification tasks frequently encounter challenges associated with class imbalance, resulting in biased model training and suboptimal classification performance. To address this issue, the combination of class decomposition a
Externí odkaz:
https://doaj.org/article/5162eda6ee064e4d9a21e8702c0b364b
Publikováno v:
Dianxin kexue, Vol 39, Pp 105-113 (2023)
Progressive growing of generative adversarial networks (PGGAN) is an adversarial network model that can generate high-resolution images.However, when the categories of samples are unbalanced, or the categories of samples are too similar or too dissim
Externí odkaz:
https://doaj.org/article/b2ba8e9114624f01896b3b918f89096a
Autor:
Elisabeth Johanna Dippold, Fuan Tsai
Publikováno v:
Sensors, Vol 24, Iss 7, p 2358 (2024)
The performance of three-dimensional (3D) point cloud reconstruction is affected by dynamic features such as vegetation. Vegetation can be detected by near-infrared (NIR)-based indices; however, the sensors providing multispectral data are resource i
Externí odkaz:
https://doaj.org/article/f2969b28451b4030be1974cd496a9334
Publikováno v:
IEEE Access, Vol 11, Pp 48697-48714 (2023)
Radio environment maps depict the coverage area of cellular networks. They are usually estimated by interpolating sparse measurements gathered in test drives. Typical estimation techniques rely on physical or statistical propagation models, known bas
Externí odkaz:
https://doaj.org/article/07b76742cc9f4538a1492ed928ad25d8
Autor:
Andi Hendra, Yasushi Kanazawa
Publikováno v:
IEEE Access, Vol 11, Pp 44176-44191 (2023)
We present a simple yet robust monocular depth estimation technique by synthesizing a depth map image from a single RGB input image using the advantage of generative adversarial networks (GAN). We employ an additional sub-model termed refiner to extr
Externí odkaz:
https://doaj.org/article/ced598226a8a47e59bf9e5a28a1e96fa
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