Alternatives and similar repositories for swg

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

• eifuentes / swae-pytorch
  Implementation of the Sliced Wasserstein Autoencoder using PyTorch
  ☆101Updated 7 years ago
• MichaelArbel / Scaled-MMD-GAN
  Scaled MMD GAN
  ☆36Updated 6 years ago
• skolouri / swae
  Implementation of the Sliced Wasserstein Autoencoders
  ☆91Updated 7 years ago
• weixsong / NormalizingFlow
  Implementation of different Normalizing Flows, NF, Planar Flows, IAF, etc.
  ☆30Updated 7 years ago
• mingzhang-yin / SIVI
  A variational inference method with accurate uncertainty estimation. It uses a new semi-implicit variational family built on neural netwo…
  ☆54Updated last year
• iffsid / disentangling-disentanglement
  ☆91Updated 6 years ago
• mducoffe / Learning-Wasserstein-Embeddings
  Keras implementation of Deep Wasserstein Embeddings
  ☆48Updated 7 years ago
• ishansd / swg
  Sliced Wasserstein Generator
  ☆39Updated 7 years ago
• sajadn / Exemplar-VAE
  Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation
  ☆69Updated 5 years ago
• conormdurkan / neural-statistician
  PyTorch Implementation of Neural Statistician
  ☆61Updated 3 years ago
• thjashin / gp-infer-net
  Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019
  ☆42Updated 3 years ago
• dilinwang820 / nonlinear_svgd
  Nonlinear SVGD for Learning Diversified Mixture Models
  ☆13Updated 6 years ago
• jshe / wasserstein-2
  ☆13Updated 6 years ago
• mingzhang-yin / ARM-gradient
  Low-variance, efficient and unbiased gradient estimation for optimizing models with binary latent variables. (ICLR 2019)
  ☆27Updated 6 years ago
• cambridge-mlg / cnn-gp
  Pytorch version of "Deep Convolutional Networks as shallow Gaussian Processes" by Adrià Garriga-Alonso, Carl Rasmussen and Laurence Aitch…
  ☆32Updated 5 years ago
• tvayer / SGW
  Code for Sliced Gromov-Wasserstein
  ☆69Updated 6 years ago
• vivienseguy / Large-Scale-OT
  Stochastic algorithms for computing Regularized Optimal Transport
  ☆58Updated 7 years ago
• ColinQiyangLi / LConvNet
  Implementation of Methods Proposed in Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks (NeurIPS 2019)
  ☆36Updated 5 years ago
• kefirski / bdir_vae
  simple implementation of "Improved Variational Inference with Inverse Autoregressive Flow" paper with pytorch
  ☆52Updated 8 years ago
• geniki / neural-processes
  PyTorch implementation of Neural Processes
  ☆88Updated 6 years ago
• didriknielsen / pixelcnn_flow
  Code for paper "Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow"
  ☆19Updated 5 years ago
• cunningham-lab / igr
  ☆30Updated 5 years ago
• eitanrich / torch-mfa
  Train a Mixture of Factor Analyzers (MFA) / Mixture of Probabilistic PCA (MPPCA) - low-rank-plus-diagonal GMMs using pytorch
  ☆41Updated 3 years ago
• ehoogeboom / emerging
  ☆42Updated 6 years ago
• AMLab-Amsterdam / FNP
  Implementation of the Functional Neural Process models
  ☆42Updated 5 years ago
• Alfo5123 / Variational-Sparse-Coding
  Reproducing the paper "Variational Sparse Coding" for the ICLR 2019 Reproducibility Challenge
  ☆62Updated 2 years ago
• pomonam / NoisyNaturalGradient
  TensorFlow implementation of "noisy K-FAC" and "noisy EK-FAC".
  ☆60Updated 6 years ago
• asteroidhouse / self-tuning-networks
  Code for Self-Tuning Networks (ICLR 2019) https://arxiv.org/abs/1903.03088
  ☆60Updated 6 years ago
• microsoft / deterministic-variational-inference
  Sample code for running deterministic variational inference to train Bayesian neural networks
  ☆102Updated 7 years ago
• CW-Huang / NAF
  Experiments for the Neural Autoregressive Flows paper
  ☆125Updated 4 years ago