vae-implementation

There are 1 repository under vae-implementation topic.

AntixK / PyTorch-VAE
A Collection of Variational Autoencoders (VAE) in PyTorch.
pytorch pytorch-implementation vae vae-implementation deep-learning reproducible-research paper-implementations pytorch-vae variational-autoencoders architecture beta-vae vqvae gumbel-softmax celeba-dataset iwae wae dfc-vae
Language:Python 5972
clementchadebec / benchmark_VAE
Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
vae benchmarking beta-vae comparison normalizing-flows pixel-cnn pytorch reproducibility reproducible-research vae-gan vae-implementation vae-pytorch variational-autoencoder vq-vae wasserstein-autoencoder
Language:Python 1677
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.
deep-learning deep-neural-networks deeplearning variational-autoencoder continuous-bernoulli deeplearning-ai vae-implementation autoencoders c cpp vae variational-autoencoders point-estimator test-statistic simulator statistical-analysis
Language:C++ 23
julian-carpenter / beta-TCVAE
Tensorflow 2.x implementation of the beta-TCVAE (arXiv:1802.04942).
vae-implementation tensorflow2 unsupervised-learning deep-learning
Language:Python 13
vsimkus / pmr2024-vae
An official repository for a VAE tutorial of Probabilistic Modelling and Reasoning (2023/2024) - a University of Edinburgh master's course.
probabilistic-models probabilistic-inference vae vae-implementation variational-inference variational-autoencoder gradient-estimation
Language:Jupyter Notebook 8
insdout / GMVAE-pytorch
Pytorch implementation of Gaussian Mixture Variational Autoencoder GMVAE
gaussian-mixture-models gmvae latent-space mnist pytorch vae-implementation vae-pytorch variational-autoencoder
Language:Python 4
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.
vae vae-implementation cvae deep-learning deep-neural-networks
Language:Python 4
raulorteg / SmileCVAE
Implementation of CVAE. Trained CVAE on faces from UTKFace Dataset to produce synthetic faces with a given degree of happiness/smileyness.
vae-pytorch vae-implementation cvae generative-model deep-learning
Language:Python 4
ayulockin / deepgenerativemodeling
Towards Generative Modeling from (variational) Autoencoder to DCGAN
autoencoder vae vae-implementation vae-gan keras keras-tensorflow autoencoder-architecture
Language:Jupyter Notebook 3
Baukebrenninkmeijer / Variational-Autoencoder-Pytorch
Implementation of the variational autoencoder with PyTorch and Fastai
vae variational-autoencoder variational-autoencoders fastai pytorch vae-implementation
Language:Jupyter Notebook 3
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
crf-model em-algorithm hmm-viterbi-algorithm log-linear-model n-grams natural-language-processing pcfg self-attention self-attentive-rnn vae-implementation
Language:Python 3
shreyansh26 / Sentence-VAE
A re-implementation of the Sentence VAE paper, Generating Sentences from a Continuous Space
gru rnn text-generation text-generation-using-rnn vae vae-implementation vae-pytorch
Language:Python 2
TimoFlesch / Autoencoders
Autoencoders (Standard, Convolutional, Variational), implemented in tensorflow
tensorflow-r1 deep-learning autoencoder vae-implementation machine-learning unsupervised-learning
Language:Python 2
DelTA-Lab-IITK / PDUN
Probabilistic framework for solving Visual Dialog
uncertainty-neural-networks uncertainty-analysis uncertainty-visualisation vae vae-implementation diversity diversity-measurement visual-dialog visual-question-answering visual-questions-generation
1
kleinzcy / Variational-AutoEncoder
VAE and CVAE pytorch implement based on MNIST
vae-implementation vae cvae vae-pytorch
Language:Jupyter Notebook 1
m4rtinkoenig / ML-with-Python
Classical Machine Learning Algorithms
machine-learning machine-learning-algorithms machine-learning-python logistic-regression knn-classification decision-trees random-forest random-forest-regression gradient-boosting gradient-boosting-classifier autoencoders autoencoders-fashionmnist variational-autoencoder vae vae-implementation vae-cnn vae-gmm vae-vamp-prior
Language:Jupyter Notebook 1
nandiniigarg / ColorVAE
ColorVAE is a Vanilla Auto Encoder (V.A.E.) which can be used to add colours to black and white images.
autoencoder cnn-architecture computer-vision deep-learning python vae-cnn vae-implementation
Language:Jupyter Notebook 1
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".
autoencoder folk-music generative-model latent-space music music-composition musical-analysis vae-implementation
Language:Python 0
bmanczak / dl_practicals
This repo is devoted to the pracicals of the course Deep Learning (5204DLFV6Y) realised at the Univeristy of Amsterdam, Fall 2020.
vae-implementation gan-implementations lstm bi-lstm vgg13 mlp
Language:TeX 0
dikshantsagar / VAE-pytorch
An implementation of Variational Auto-encoder with TSNE Visualization on MNIST dataset.
vae-implementation vae tsne representation-learning
Language:Jupyter Notebook 0
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.
computer-vision cyclegan-pytorch gan gan-pytorch mnist pytorch svhn-dataset vae-implementation vae-pytorch vibot
Language:Jupyter Notebook 0
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.
arma deepar multihead-attention time-series vae-implementation vae-pytorch reproducibility-challenge comp6248
Language:Jupyter Notebook 0
SwikarGautam / VAE
Variational Autoencoder (VAE) trained on MNIST
vae-implementation autoencoder-mnist numpy
Language:Python 0
UW-CIA / Models_at_edge
Running VAEs on mobile and IOT devices using TFLite.
vae-implementation tflite edge-machine-learning
Language:Jupyter Notebook 0
alicika / MLExamination
The CNN implementation to qualify images. This repo also contains Japanese coin validation(with binaries) and MNIST challenge detection.
python vae-implementation cnn-keras
Language:Python
AmanPriyanshu / SynthesizeDaten
A repository for generating synthetic data (images) using various DL/ML models.
gans gans-models vae vae-pytorch vae-implementation dbn dbnet synthetic-data synthetic-dataset-generation
Language:Python
animesh-007 / Basic_VAE_implementation
Basic implementation of VAE
pytorch vae-implementation autoencoder
Language:Python
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.
cgan-training matlab pytorch tensorflow vae-implementation
Language:Jupyter Notebook
cankobanz / VAE-and-GAN-training-on-MNIST-dataset
Handwritten Digit Generation with VAE and GAN are applied.
gan generative-adversarial-network keras keras-tensorflow vae vae-gan vae-implementation variational-autoencoder
Language:Python
DrLux / VAE-on-VizdoomTakeCover
Convolutional Variational Autoencoder on VizdoomTakeCover
vae-implementation vizdoom tensorflow-vae
Language:Jupyter Notebook
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.
gaussian-processes gpytorch model-predictive-controller mppi pytorch robot-learning vae-implementation robot-manipulation trajectory-optimization
Language:Jupyter Notebook
jainraj / Advanced-Deep-Representation-Learning-IISc
adversarial-discriminative-domain-adaptation bitmoji celeba-dataset constrastive-learning cyclegan domain-adaptation dsprites-data energy-based-model gan vae vae-cnn vae-implementation vae-pytorch wasserstein-gan
Language:Jupyter Notebook
manikanta5557 / Variational-Auto-Encoders-Mnist
A simple implementation of variational Auto encoders using Mnist dataset in tensorflow.
open3d opencv point-cloud tensorflow vae vae-implementation mnist cv2 python3
Language:Jupyter Notebook
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.
vae-implementation
Language:Jupyter Notebook
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.
python3 streamlit vae-implementation vae-pytorch
Language:Python
WindowsKonon1337 / VAE-Face-Generator
Simple VAE face generator
image-generation nn pytorch vae-implementation vae-pytorch convolutional-neural-networks neural-network variational-autoencoder
Language:Jupyter Notebook

vae-implementation

AntixK / PyTorch-VAE

clementchadebec / benchmark_VAE

meiyulee / continuous_Bernoulli

julian-carpenter / beta-TCVAE

vsimkus / pmr2024-vae

insdout / GMVAE-pytorch

ioangatop / DeepGenerativeModels

raulorteg / SmileCVAE

ayulockin / deepgenerativemodeling

Baukebrenninkmeijer / Variational-Autoencoder-Pytorch

keya-desai / Natural-Language-Processing

shreyansh26 / Sentence-VAE

TimoFlesch / Autoencoders

DelTA-Lab-IITK / PDUN

kleinzcy / Variational-AutoEncoder

m4rtinkoenig / ML-with-Python

nandiniigarg / ColorVAE

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

bmanczak / dl_practicals

dikshantsagar / VAE-pytorch

GraceSevillano / Advanced-Image-Analysis-Assignments

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

SwikarGautam / VAE

UW-CIA / Models_at_edge

alicika / MLExamination

AmanPriyanshu / SynthesizeDaten

animesh-007 / Basic_VAE_implementation

ayseirmak / Chatter-Detection

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

DrLux / VAE-on-VizdoomTakeCover

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

jainraj / Advanced-Deep-Representation-Learning-IISc

manikanta5557 / Variational-Auto-Encoders-Mnist

prkhrv / Variational-Auto-Encoders-VAEs

trinity652 / VAE-Face-generator

WindowsKonon1337 / VAE-Face-Generator