Climatereconstructionai
Software to train/evaluate models to reconstruct missing values in climate data (e.g., HadCRUT4) based on a U-Net with partial convolutions
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Cnn Watermark Removal | 911 | 3 years ago | 20 | Python | ||||||
| Fully convolutional deep neural network to remove transparent overlays from images | ||||||||||
| Deepfovea | 364 | 3 years ago | 1 | other | PureBasic | |||||
| Neural Reconstruction for Foveated Rendering and Video Compression using Learned Statistics of Natural Videos | ||||||||||
| Deep_image_prior | 146 | 4 years ago | apache-2.0 | Python | ||||||
| Image reconstruction done with untrained neural networks. | ||||||||||
| Hand Reconstruction | 66 | 4 years ago | 1 | other | Jupyter Notebook | |||||
| Single Image 3D Hand Reconstruction with Mesh Convolutions | ||||||||||
| Complex Networks Release | 61 | 3 years ago | 2 | Python | ||||||
| Implementation related to the paper "Complex-Valued Convolutional Neural Networks for MRI Reconstruction" by Elizabeth K. Cole et. al; Toolbox for complex-valued convolution and activation functions using an unrolled architecture. | ||||||||||
| Vcmeshconv | 56 | 3 years ago | 4 | other | C++ | |||||
| Learning latent representations of registered meshes is useful for many 3D tasks. Techniques have recently shifted to neural mesh autoencoders. Although they demonstrate higher precision than traditional methods, they remain unable to capture fine-grained deformations. Furthermore, these methods can only be applied to a template-specific surface mesh, and is not applicable to more general meshes, like tetrahedrons and non-manifold meshes. While more general graph convolution methods can be employed, they lack performance in reconstruction precision and require higher memory usage. In this paper, we propose a non-template-specific fully convolutional mesh autoencoder for arbitrary registered mesh data. It is enabled by our novel convolution and (un)pooling operators learned with globally shared weights and locally varying coefficients which can efficiently capture the spatially varying contents presented by irregular mesh connections. Our model outperforms state-of-the-art methods on reconstruction accuracy. In addition, the latent codes of our network are fully localized thanks to the fully convolutional structure, and thus have much higher interpolation capability than many traditional 3D mesh generation models. | ||||||||||
| Climatereconstructionai | 50 | 4 months ago | 1 | bsd-3-clause | Python | |||||
| Software to train/evaluate models to reconstruct missing values in climate data (e.g., HadCRUT4) based on a U-Net with partial convolutions | ||||||||||
| Spiralnet_plus | 37 | 4 years ago | mit | Python | ||||||
| The project is an official implementation of our paper "SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator" (ICCV-W 2019) | ||||||||||
| Singan Tensorflow2.0 | 13 | 5 years ago | Python | |||||||
| simple implementation of SinGAN on tensorflow2.0 | ||||||||||
| Deepmri | 10 | 4 years ago | 2 | gpl-3.0 | Python | |||||
| The code for paper 'DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution' | ||||||||||
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Python
Reconstruction
Convolution