Alternatives and similar repositories for vaesbd:

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

• pratikm141 / MMD-Variational-Autoencoder-Pytorch-InfoVAE
  Implementation of the MMD VAE paper (InfoVAE: Information Maximizing Variational Autoencoders) in pytorch
  ☆42Updated 4 years ago
• stelzner / monet
  An implementation of the MONet model for unsupervised scene decomposition in PyTorch
  ☆58Updated 2 years ago
• pemami4911 / EfficientMORL
  EfficientMORL (ICML'21)
  ☆22Updated 3 years ago
• joelouismarino / iterative_inference
  Implementation of iterative inference in deep latent variable models
  ☆43Updated 5 years ago
• kefirski / variational_dropout
  Implementation of "Variational Dropout and the Local Reparameterization Trick" paper with Pytorch
  ☆50Updated 7 years ago
• rr-learning / disentanglement_dataset
  ☆75Updated 9 months ago
• xqding / Importance_Weighted_Autoencoders
  A PyTorch Implementation of the Importance Weighted Autoencoders
  ☆40Updated 6 years ago
• aakhundov / tf-attend-infer-repeat
  TensorFlow-based implementation of "Attend, Infer, Repeat" paper (Eslami et al., 2016, arXiv:1603.08575).
  ☆42Updated 7 years ago
• fabienbaradel / cophy
  "CoPhy: Counterfactual Learning of Physical Dynamics", F. Baradel, N. Neverova, J. Mille, G. Mori, C. Wolf, ICLR'2020
  ☆33Updated 4 years ago
• Zhiyuan1991 / proVLAE
  ☆27Updated 4 years ago
• lrjconan / RBP
  Recurrent Back Propagation, Back Propagation Through Optimization, ICML 2018
  ☆41Updated 6 years ago
• vlievin / biva-pytorch
  Official PyTorch BIVA implementation (BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling)
  ☆83Updated 2 years ago
• haoliuhl / hybrid-discriminative-generative
  Hybrid Discriminative-Generative Training via Contrastive Learning
  ☆75Updated last year
• sajadn / Exemplar-VAE
  Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation
  ☆69Updated 4 years ago
• enalisnick / mixture_density_VAEs
  Variational Autoencoders with Gaussian Mixture Latent Space
  ☆36Updated 7 years ago
• hschoi1 / rich_latent
  rich posterior approximations and anomaly detection
  ☆20Updated 5 years ago
• thjashin / gp-infer-net
  Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019
  ☆41Updated 2 years ago
• baudm / MONet-pytorch
  Burgess et al. "MONet: Unsupervised Scene Decomposition and Representation"
  ☆88Updated 2 years ago
• rszeto / moving-symbols
  Like Moving MNIST, but way more flexible
  ☆24Updated 4 years ago
• 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 3 years ago
• iffsid / disentangling-disentanglement
  ☆89Updated 5 years ago
• asteroidhouse / self-tuning-networks
  Code for Self-Tuning Networks (ICLR 2019) https://arxiv.org/abs/1903.03088
  ☆53Updated 5 years ago
• insperatum / vhe
  The Variational Homoencoder: Learning to learn high capacity generative models from few examples
  ☆34Updated last year
• AIcrowd / neurips2019_disentanglement_challenge_starter_kit
  Starter Kit for the NeurIPS 2019 Disentanglement Challenge
  ☆33Updated 2 years ago
• pomonam / NoisyNaturalGradient
  TensorFlow implementation of "noisy K-FAC" and "noisy EK-FAC".
  ☆60Updated 6 years ago
• mingzhang-yin / ARM-gradient
  Low-variance, efficient and unbiased gradient estimation for optimizing models with binary latent variables. (ICLR 2019)
  ☆28Updated 5 years ago
• mmrl / disent-and-gen
  Code from the article: "The Role of Disentanglement in Generalisation" (ICLR, 2021).
  ☆22Updated 2 years ago
• Cyanogenoid / fspool
  [ICLR 2020] FSPool: Learning Set Representations with Featurewise Sort Pooling
  ☆42Updated last year
• yaohungt / Adaptive-Regularization-Neural-Network
  [NeurIPS'19] [PyTorch] Adaptive Regularization in NN
  ☆67Updated 5 years ago
• AMLab-Amsterdam / FNP
  Implementation of the Functional Neural Process models
  ☆43Updated 4 years ago