normflows
normflows: A PyTorch Package for Normalizing Flows - Published in JOSS (2023)
Multi-view-AE
Multi-view-AE: A Python package for multi-view autoencoder models - Published in JOSS (2023)
pythae
Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
rapidae
Explore, compare and develop autoencoder models with a back-end agnostic framework
vaes
Reproducible code showing the various types of variational autoencoders I have implemented
ima-vae
This is the code for the paper Embrace the Gap: VAEs perform Independent Mechanism Analysis, showing that optimizing the ELBO is equivalent to optimizing the IMA-regularized log-likelihood under certain assumptions (e.g., small decoder variance).
https://github.com/alexeyev/keras-generating-sentences-from-a-continuous-space
Text Variational Autoencoder inspired by the paper 'Generating Sentences from a Continuous Space' Bowman et al. https://arxiv.org/abs/1511.06349
https://github.com/alxhslm/hand-writing-generation
A GenAI app to generate hand-written characters
https://github.com/amacaluso/ssb-vae
Self-Supervised Bernoulli Autoencoders for Semi-Supervised Hashing: we investigate the robustness of hashing methods based on variational autoencoders to the lack of supervision, focusing on two semi-supervised approaches currently in use. In addition, we propose a novel supervision approach in which the model uses its own predictions of the label distribution to implement the pairwise objective. Compared to the best baseline, this procedure yields similar performance in fully-supervised settings but improves significantly the results when labelled data is scarce.
jamie
Joint variational Autoencoders for Multimodal Imputation and Embedding (JAMIE)
mgvae
Multiresolution Equivariant Graph Variational Autoencoder (MGVAE) https://arxiv.org/abs/2106.00967
3d_very_deep_vae
PyTorch implementations of variational autoencoders for 3D images