Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Richard T. Marriott"'
Publikováno v:
FG
Generative Adversarial Networks (GANs) are able to learn mappings between simple, relatively low-dimensional, random distributions and points on the manifold of realistic images in image-space. The semantics of this mapping, however, are typically en
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d226ab52c8ac9d1b3903451119dc1e35
https://doi.org/10.1109/fg47880.2020.00042
https://doi.org/10.1109/fg47880.2020.00042
Publikováno v:
IJCB
Generative Adversarial Networks (GANs) are now capable of producing synthetic face images of exceptionally high visual quality. In parallel to the development of GANs themselves, efforts have been made to develop metrics to objectively assess the cha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::91f8949cc75f8656331351c81f7b35d2
Publikováno v:
CVPR
Facial recognition using deep convolutional neural networks relies on the availability of large datasets of face images. Many examples of identities are needed, and for each identity, a large variety of images are needed in order for the network to l
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::670f00f6426f34f861b291584baafb28
Publikováno v:
Pattern Recognition Letters
Pattern Recognition Letters, 2018, 110, pp.44-50. ⟨10.1016/j.patrec.2018.03.024⟩
Pattern Recognition Letters, Elsevier, 2018, 110, pp.44-50. ⟨10.1016/j.patrec.2018.03.024⟩
Pattern Recognition Letters, 2018, 110, pp.44-50. ⟨10.1016/j.patrec.2018.03.024⟩
Pattern Recognition Letters, Elsevier, 2018, 110, pp.44-50. ⟨10.1016/j.patrec.2018.03.024⟩
We propose a novel algorithm for unsupervised extraction of piecewise planar models from depth-data. Among other applications, such models are a good way of enabling autonomous agents (robots, cars, drones, etc.) to effectively perceive their surroun
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::57991d3225f4f241e608d2b7b1e45271
https://hal.inria.fr/hal-01663984
https://hal.inria.fr/hal-01663984
Publikováno v:
Monthly Weather Review. 141:3331-3342
The role of observations in reducing 24-h forecast errors is evaluated using the adjoint-based forecast sensitivity to observations (FSO) method developed within the Met Office global numerical weather prediction (NWP) system. The impacts of various
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::03633c472fbdd569229c3ec28509fee5
https://doi.org/10.1175/mwr-d-12-00232.1
https://doi.org/10.1175/mwr-d-12-00232.1
Autor:
Richard T. Marriott, Andrew C. Lorenc
Publikováno v:
Quarterly Journal of the Royal Meteorological Society. 140:209-224
An adjoint-based method for calculating the impacts of observations in the Met Office's global four-dimensional variational assimilation (4D-Var) system is documented. Our approach is novel, as we seek from the outset a linearized approximation to pa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e81620864a1f4c4c138386cecdb40337
https://doi.org/10.1002/qj.2122
https://doi.org/10.1002/qj.2122