haskell-vaeLearning about Haskell with Variational Autoencoders
VAE-Gumbel-SoftmaxAn implementation of a Variational-Autoencoder using the Gumbel-Softmax reparametrization trick in TensorFlow (tested on r1.5 CPU and GPU) in ICLR 2017.
vaeganAn implementation of VAEGAN (variational autoencoder + generative adversarial network).
multimodal-vae-publicA PyTorch implementation of "Multimodal Generative Models for Scalable Weakly-Supervised Learning" (https://arxiv.org/abs/1802.05335)
vae-torchVariational autoencoder for anomaly detection (in PyTorch).
adVAEImplementation of 'Self-Adversarial Variational Autoencoder with Gaussian Anomaly Prior Distribution for Anomaly Detection'
STEPSpatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits
continuous BernoulliThere are C language computer programs about the simulator, transformation, and test statistic of continuous Bernoulli distribution. More than that, the book contains continuous Binomial distribution and continuous Trinomial distribution.
CVAE DialCVAE_XGate model in paper "Xu, Dusek, Konstas, Rieser. Better Conversations by Modeling, Filtering, and Optimizing for Coherence and Diversity"
OCDVAEContinualLearningOpen-source code for our paper: Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition
pyroVEDInvariant representation learning from imaging and spectral data
BagelIPCCC 2018: Robust and Unsupervised KPI Anomaly Detection Based on Conditional Variational Autoencoder
AC-VRNNPyTorch code for CVIU paper "AC-VRNN: Attentive Conditional-VRNN for Multi-Future Trajectory Prediction"
deep-blueberryIf you've always wanted to learn about deep-learning but don't know where to start, then you might have stumbled upon the right place!
vae-concreteKeras implementation of a Variational Auto Encoder with a Concrete Latent Distribution
SIVIUsing neural network to build expressive hierarchical distribution; A variational method to accurately estimate posterior uncertainty; A fast and general method for Bayesian inference. (ICML 2018)
svae cf[ WSDM '19 ] Sequential Variational Autoencoders for Collaborative Filtering
benchmark VAEUnifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
soft-intro-vae-pytorch[CVPR 2021 Oral] Official PyTorch implementation of Soft-IntroVAE from the paper "Soft-IntroVAE: Analyzing and Improving Introspective Variational Autoencoders"
MIDI-VAENo description or website provided.