vae-implementation

clementchadebec/benchmark_VAE

Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)

meiyulee/continuous_Bernoulli

There 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.

julian-carpenter/beta-TCVAE

Tensorflow 2.x implementation of the beta-TCVAE (arXiv:1802.04942).

insdout/GMVAE-pytorch

Pytorch implementation of Gaussian Mixture Variational Autoencoder GMVAE

vsimkus/pmr2024-vae

An official repository for a VAE tutorial of Probabilistic Modelling and Reasoning (2023/2024) - a University of Edinburgh master's course.

keya-desai/Natural-Language-Processing

Python implementation of N-gram Models, Log linear and Neural Linear Models, Back-propagation and Self-Attention, HMM, PCFG, CRF, EM, VAE

Baukebrenninkmeijer/Variational-Autoencoder-Pytorch

Implementation of the variational autoencoder with PyTorch and Fastai

ioangatop/DeepGenerativeModels

Variational Auto Encoders (VAEs), Generative Adversarial Networks (GANs) and Generative Normalizing Flows (NFs) and are the most famous and powerful deep generative models.

raulorteg/SmileCVAE

Implementation of CVAE. Trained CVAE on faces from UTKFace Dataset to produce synthetic faces with a given degree of happiness/smileyness.

ayulockin/deepgenerativemodeling

Towards Generative Modeling from (variational) Autoencoder to DCGAN

shreyansh26/Sentence-VAE

A re-implementation of the Sentence VAE paper, Generating Sentences from a Continuous Space

jainraj/Advanced-Deep-Representation-Learning-IISc

TimoFlesch/Autoencoders

Autoencoders (Standard, Convolutional, Variational), implemented in tensorflow

xinli2008/VAE_from_scratch

Pytorch版本实现的VAE和公式推导

ayseirmak/Chatter-Detection

Utilized a VAE (Variational Autoencoder) and CGAN (Conditional Generative Adversarial Network) models to generate synthetic chatter signals, addressing the challenge of imbalanced data in turning operations. Compared othe performance of synthetic chatter signals.

DelTA-Lab-IITK/PDUN

Probabilistic framework for solving Visual Dialog

HariKrishnan06082k/Robot-Learning-for-Planning-and-Control

Topics include function approximation, learning dynamics, using learned dynamics in control and planning, handling uncertainty in learned models, learning from demonstration, and model-based and model-free reinforcement learning.

kleinzcy/Variational-AutoEncoder

VAE and CVAE pytorch implement based on MNIST

lars-chen/3D-VAE

Implementation of 3D convolutional conditional variational autoencoder.

nandiniigarg/ColorVAE

ColorVAE is a Vanilla Auto Encoder (V.A.E.) which can be used to add colours to black and white images.

Resh-97/MixSeq-Connecting-Macroscopic-Time-Series-Forecasting-with-Microscopic-Time-Series-Data

Testing the Reproducibility of the paper: MixSeq. Under the assumption that macroscopic time series follow a mixture distribution, they hypothesise that lower variance of constituting latent mixture components could improve the estimation of macroscopic time series.

Shreshth-112/Video-summarization-using-keyframe-extraction

Built a model to create highlights/summary of given video. The results of this study shows that, with a remarkable similarity index(SSIM) of 98%, the recommended technique is quite successful in choosing keyframes that are both educational and distinctive from the original movie

UW-CIA/Models_at_edge

Running VAEs on mobile and IOT devices using TFLite.

amerotz/latent-representations-for-traditional-music-analysis-and-generation

This repository contains the code, data and scripts used to write the Bachelor Thesis "Latent representations for traditional music analysis and generation".

ericyangchen/conditional-VAE-for-video-prediction

Implementing a Conditional VAE for video prediction with PyTorch

GraceSevillano/Advanced-Image-Analysis-Assignments

Solutions for Advanced Image Analysis course assignments, featuring model designs for image summation and generation with MNIST, and style transfer using CycleGAN with MNIST and SVHN datasets.

mode1990/Linear-Deep-Learning-Latent-Representation-for-iOligo-lineage-scRNAseq-

Leveraging the power of LD variational autoencoders to identify latent representations as dim red embeddings of sc data

sathya-ml/multimodal-vrnn-vae

A PyTorch implementation of multimodal VRNN and VAE.

trinity652/VAE-Face-generator

A variational Autoencoder (VAE) to generate human faces based on the CelebA dataset. A VAE is a generative model that learns to represent high-dimensional data (like images) in a lower-dimensional latent space, and then generates new data from this space.

AmanPriyanshu/SynthesizeDaten

A repository for generating synthetic data (images) using various DL/ML models.

cankobanz/VAE-and-GAN-training-on-MNIST-dataset

Handwritten Digit Generation with VAE and GAN are applied.

manikanta5557/Variational-Auto-Encoders-Mnist

A simple implementation of variational Auto encoders using Mnist dataset in tensorflow.

prkhrv/Variational-Auto-Encoders-VAEs

A variational autoencoder can be defined as being an autoencoder whose training is regularised to avoid overfitting and ensure that the latent space has good properties that enable generative process.

Tumuluri007/Generative-Models

Encoder and Decoder in VAE's.

WindowsKonon1337/VAE-Face-Generator

Simple VAE face generator