Alternatives and similar repositories for pytorch-vae

Users that are interested in pytorch-vae are comparing it to the libraries listed below

• vlievin / biva-pytorch
  Official PyTorch BIVA implementation (BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling)
  ☆84Updated 2 years ago
• AMLab-Amsterdam / FNP
  Implementation of the Functional Neural Process models
  ☆42Updated 5 years ago
• sajadn / Exemplar-VAE
  Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation
  ☆69Updated 4 years ago
• wjmaddox / drbayes
  Code Repo for "Subspace Inference for Bayesian Deep Learning"
  ☆83Updated last year
• izmailovpavel / flowgmm
  ☆148Updated 3 years ago
• soobinseo / Attentive-Neural-Process
  A Pytorch Implementation of Attentive Neural Process
  ☆75Updated 6 years ago
• microsoft / deterministic-variational-inference
  Sample code for running deterministic variational inference to train Bayesian neural networks
  ☆100Updated 6 years ago
• xqding / Importance_Weighted_Autoencoders
  A PyTorch Implementation of the Importance Weighted Autoencoders
  ☆39Updated 6 years ago
• conormdurkan / neural-statistician
  PyTorch Implementation of Neural Statistician
  ☆60Updated 3 years ago
• thjashin / gp-infer-net
  Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019
  ☆41Updated 2 years ago
• singhgautam / snp
  This is the official source code for Sequential Neural Processes.
  ☆40Updated 2 years ago
• maremun / swg
  Sliced Wasserstein Generator
  ☆23Updated 6 years ago
• CW-Huang / NAF
  Experiments for the Neural Autoregressive Flows paper
  ☆125Updated 4 years ago
• iffsid / disentangling-disentanglement
  ☆91Updated 6 years ago
• widedeepnetworks / widedeepnetworks
  Code for the paper Gaussian process behaviour in wide deep networks
  ☆46Updated 7 years ago
• ermongroup / f-EBM
  Code for "Training Deep Energy-Based Models with f-Divergence Minimization" ICML 2020
  ☆36Updated 2 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 11 months ago
• kingofspace0wzz / wae-rnf-lm
  Pytorch Implemetation for our NAACL2019 Paper "Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling" http…
  ☆63Updated 5 years ago
• nicola-decao / BNAF
  Pytorch implementation of Block Neural Autoregressive Flow
  ☆181Updated 4 years ago
• ssydasheng / FBNN
  Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)
  ☆85Updated 5 years ago
• denproc / Taming-VAEs
  ☆26Updated 6 years ago
• MichaelArbel / GeneralizedEBM
  ☆54Updated last year
• LuisArmandoPerez / DiffusionVAE
  ☆29Updated 3 years ago
• SebFar / radial_bnn
  Code to accompany the paper Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning
  ☆33Updated 5 years ago
• agadetsky / pytorch-pl-variance-reduction
  [AAAI 2020 Oral] Low-variance Black-box Gradient Estimates for the Plackett-Luce Distribution
  ☆38Updated 4 years ago
• riannevdberg / sylvester-flows
  ☆181Updated 6 years ago
• HolyBayes / pytorch_ard
  Pytorch implementation of Variational Dropout Sparsifies Deep Neural Networks
  ☆84Updated 3 years ago
• team-approx-bayes / dnn2gp
  Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)
  ☆48Updated 5 years ago
• abdulfatir / normalizing-flows
  Understanding normalizing flows
  ☆132Updated 5 years ago
• EmilienDupont / neural-processes
  Pytorch implementation of Neural Processes for functions and images
  ☆233Updated 3 years ago