Alternatives and similar repositories for misgan

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

• probabilistic-learning / HI-VAE
  ☆91Updated 2 years ago
• lstruski / Processing-of-missing-data-by-neural-networks
  ☆43Updated 6 years ago
• Harry24k / MIDA-pytorch
  PyTorch implementation of "MIDA: Multiple Imputation using Denoising Autoencoders"
  ☆28Updated 6 years ago
• pamattei / miwae
  Implementation of the MIWAE method for deep generative modelling of incomplete data sets.
  ☆41Updated last year
• mkocaoglu / CausalGAN
  ☆144Updated 8 years ago
• zhiyongc / GRU-D
  Gated Recurrent Unit with a Decay mechanism for Multivariate Time Series with Missing Values
  ☆119Updated 6 years ago
• rcamino / imputation-dgm
  Code for the paper "Improving Missing Data Imputation with Deep Generative Models"
  ☆32Updated 6 years ago
• ermongroup / ssdkl
  Code that accompanies the paper Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance
  ☆76Updated 6 years ago
• mtsang / neural-interaction-detection
  Detecting Statistical Interactions from Neural Network Weights
  ☆49Updated 5 years ago
• sjblim / rmsn_nips_2018
  ☆40Updated 6 years ago
• mmcdermott / CWR-GAN
  The code for the cycle wasserstein regression generative adversarial network model for semi supervised bi-directional regression.
  ☆24Updated 7 years ago
• jsyoon0823 / GAIN
  Codebase for Generative Adversarial Imputation Networks (GAIN) - ICML 2018
  ☆402Updated 3 years ago
• ananyahjha93 / multi-level-vae
  This repository contains a pytorch implementation for the paper: Multi-Level Variational Autoencoder (https://arxiv.org/abs/1705.08841), …
  ☆71Updated 3 years ago
• rcamino / multi-categorical-gans
  Code for the paper "Generating Multi-Categorical Samples with Generative Adversarial Networks"
  ☆49Updated 2 years ago
• MarkPKCollier / MissingDataVAEs
  ☆19Updated 5 years ago
• tijiang13 / Semi-supervised-VAE
  ☆24Updated 6 years ago
• xiaoshuai09 / Wasserstein-Learning-For-Point-Process
  learning point processes by means of optimal transport and wasserstein distance
  ☆54Updated 7 years ago
• jsyoon0823 / INVASE
  Codebase for INVASE: Instance-wise Variable Selection - 2019 ICLR
  ☆63Updated 5 years ago
• xuwd11 / Dropout_Tutorial_in_PyTorch
  Dropout as Regularization and Bayesian Approximation
  ☆58Updated 6 years ago
• ratschlab / SOM-VAE
  TensorFlow implementation of the SOM-VAE model as described in https://arxiv.org/abs/1806.02199
  ☆197Updated 2 years ago
• djsutherland / opt-mmd
  Learning kernels to maximize the power of MMD tests
  ☆210Updated 7 years ago
• lopezpaz / classifier_tests
  Code for "Revisiting classifier two-sample tests" (ICLR 2017).
  ☆20Updated 7 years ago
• bfarzin / pytorch_aae
  Pytorch Adversarial Auto Encoder (AAE)
  ☆87Updated 6 years ago
• chaoshangcs / VIGAN
  PyTorch implementation of VIGAN
  ☆40Updated 7 years ago
• slim1017 / VaDE
  Python code for paper - Variational Deep Embedding : A Generative Approach to Clustering
  ☆305Updated 7 years ago
• Han-JD / GRU-D
  inspired by 'Recurrent Neural Networks for Multivariate Time Series with Missing Values' pytorch ver
  ☆117Updated 6 years ago
• eifuentes / swae-pytorch
  Implementation of the Sliced Wasserstein Autoencoder using PyTorch
  ☆102Updated 6 years ago
• Kyushik / Predictive-Uncertainty-Estimation-using-Deep-Ensemble
  This repository is the code for Predictive Uncertainty Estimation using Deep Ensemble
  ☆157Updated 3 years ago
• zhampel / clusterGAN
  Pytorch Implementation of ClusterGAN (arXiv:1809.03627)
  ☆96Updated 2 years ago
• sedelmeyer / wasserstein-auto-encoder
  A brief tutorial on the Wasserstein auto-encoder
  ☆83Updated 6 years ago