Alternatives and similar repositories for deepmesh

Users that are interested in deepmesh are comparing it to the libraries listed below

• bwohlberg / sporco-cuda
  CUDA extension for the SPORCO project
  ☆18Updated 3 years ago
• mughanibu / Deep-Learning-for-Inverse-Problems
  This project hosts the code and datasets I used for Deep Learning course at Boston University. It aims to post-process the images the low…
  ☆41Updated 8 years ago
• kkothari93 / fionet
  Official repo for "Learning the geometry of wave-based imaging"
  ☆12Updated 4 years ago
• bwohlberg / sporco-notebooks
  Jupyter notebook usage examples for the SPORCO project
  ☆19Updated 3 years ago
• adler-j / wasserstein_inverse_problems
  Code for the article "Learning to solve inverse problems using Wasserstein loss"
  ☆28Updated 7 years ago
• lunz-s / DeepAdverserialRegulariser
  ☆18Updated 5 years ago
• tomas-angles / generative-scattering-networks
  ☆29Updated 5 years ago
• sihan-zeng / f-csrg
  Tensorflow implementation of the paper "Fast Compressive Sensing Using Generative Model with Structed Latent Variables"
  ☆10Updated 5 years ago
• kwinkunks / timefreak
  Time-frequency decomposition
  ☆20Updated 5 months ago
• ehoogeboom / convolution_exponential_and_sylvester
  ☆28Updated 3 years ago
• Schlumberger / pixel-constrained-cnn-pytorch
  Pytorch implementation of Pixel Constrained CNN, a framework for probabilistic semantic inpainting
  ☆33Updated last year
• lin-shuyu / ladder-latent-data-distribution-modelling
  In this paper, we show that the performance of a learnt generative model is closely related to the model's ability to accurately represen…
  ☆41Updated 4 years ago
• ermongroup / sparse_gen
  Code for "Modeling Sparse Deviations for Compressed Sensing using Generative Models", ICML 2018
  ☆24Updated 6 years ago
• juliagusak / neural-ode-metasolver
  Supplementary code for the paper "Meta-Solver for Neural Ordinary Differential Equations" https://arxiv.org/abs/2103.08561
  ☆25Updated 4 years ago
• oracleofnj / spectral-repr-cnns
  Implementation of Rippel, Snoek, and Adams 2015 (https://arxiv.org/pdf/1506.03767.pdf)
  ☆38Updated 6 years ago
• doerlbh / UnsupervisedAttentionMechanism
  Code for our paper: "Regularity Normalization: Neuroscience-Inspired Unsupervised Attention across Neural Network Layers".
  ☆21Updated 3 years ago
• rileyedmunds / complexcnn
  research on convolutional neural networks in fourier space
  ☆15Updated 8 years ago
• LukasMosser / DeepFlow
  Pytorch implementation of "DeepFlow: History Matching in the Space of Deep Generative Models"
  ☆34Updated 5 years ago
• pputzky / invertible_rim
  Official Code for "Invert to Learn to Invert" that allows training of invertible networks without storing activations
  ☆37Updated 5 years ago
• LuisArmandoPerez / DiffusionVAE
  ☆29Updated 3 years ago
• permfl / dictlearn
  Dictionary Learning for image processing
  ☆35Updated 4 years ago
• jinxu06 / gsubsampling
  Reference implementation for "Group Equivariant Subsampling"
  ☆16Updated 3 years ago
• FurongHuang / ConvDicLearnTensorFactor
  Tensor methods have emerged as a powerful paradigm for consistent learning of many latent variable models such as topic models, independe…
  ☆17Updated 8 years ago
• universome / non-uniform-interpolation
  Differentiable non-uniform interpolation: https://arxiv.org/abs/2012.13257
  ☆10Updated 3 years ago
• zj10 / PGA
  A TensorFlow implementation of perceptual generative autoencoder (PGA).
  ☆23Updated 4 years ago
• rodms / opticalfibreml
  Data and code for the paper "Deep, complex, invertible networks for inversion of transmission effects in multimode optical fibres" publis…
  ☆18Updated 6 years ago
• stefanradev93 / cINN
  Contains legacy code and model examples for the paper "BayesFlow: Learning complex stochastic models with invertible neural networks"
  ☆23Updated 4 years ago
• adler-j / learned_gradient_tomography
  Solving ill-posed inverse problems using iterative deep neural networks
  ☆95Updated 3 years ago
• convnets-as-gps / convnets-as-gps
  Code for "Deep Convolutional Networks as shallow Gaussian Processes"
  ☆16Updated 6 years ago
• DavideNardone / Greedy-Adaptive-Dictionary
  Greedy Adaptive Dictionary (GAD) is a learning algorithm that sets out to find sparse atoms for speech signals.
  ☆11Updated 6 years ago