Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Tamar Rott Shaham"'
Publikováno v:
Buildings, Vol 13, Iss 7, p 1793 (2023)
In interior space planning, the furnishing stage usually entails manual iterative processes, including meeting design objectives, incorporating professional input, and optimizing design performance. Machine learning has the potential to automate and
Externí odkaz:
https://doaj.org/article/db986a24a89d4c24ab3ea16883355485
Publikováno v:
2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b8ccbd07466ae8dd35dc3f1adddb5ff3
https://doi.org/10.1109/wacv51458.2022.00011
https://doi.org/10.1109/wacv51458.2022.00011
Publikováno v:
CVPR
We introduce a new generator architecture, aimed at fast and efficient high-resolution image-to-image translation. We design the generator to be an extremely lightweight function of the full-resolution image. In fact, we use pixel-wise networks; that
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f9961c48c1ba52c5c57febba00e07ebd
https://doi.org/10.1109/cvpr46437.2021.01464
https://doi.org/10.1109/cvpr46437.2021.01464
Publikováno v:
ICCV
We introduce SinGAN, an unconditional generative model that can be learned from a single natural image. Our model is trained to capture the internal distribution of patches within the image, and is then able to generate high quality, diverse samples
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::765f61ba6728f48195c5ca37fd78a42c
https://doi.org/10.1109/iccv.2019.00467
https://doi.org/10.1109/iccv.2019.00467
Publikováno v:
CVPR
In recent years, deep neural networks (DNNs) achieved unprecedented performance in many low-level vision tasks. However, state-of-the-art results are typically achieved by very deep networks, which can reach tens of layers with tens of millions of pa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fd8627d86d157e09dd29c41a8d494e95
Autor:
Tomer Michaeli, Tamar Rott Shaham
Publikováno v:
Computer Vision – ECCV 2016 ISBN: 9783319464657
ECCV (6)
ECCV (6)
Image priors play a key role in low-level vision tasks. Over the years, many priors have been proposed, based on a wide variety of principles. While different priors capture different geometric properties, there is currently no unified approach to in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8067eeb3260dcffd57f7cfaa8c2baf1f
https://doi.org/10.1007/978-3-319-46466-4_9
https://doi.org/10.1007/978-3-319-46466-4_9