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Found 4 papers, 2 papers with code
AtmoDist: Self-supervised Representation Learning for Atmospheric Dynamics
Representation learning has proven to be a powerful methodology in a wide variety of machine learning applications.
Towards Representation Learning for Atmospheric Dynamics
The prediction of future climate scenarios under anthropogenic forcing is critical to understand climate change and to assess the impact of potentially counter-acting technologies.
Inpainting-based Video Compression in FullHD
Compression methods based on inpainting are an evolving alternative to classical transform-based codecs for still images.
Optimising Spatial and Tonal Data for PDE-based Inpainting
Moreover, is more generic than other data optimisation approaches for the sparse inpainting problem, since it can also be extended to nonlinear inpainting operators such as EED.
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