beta-vae

There are 5 repositories under beta-vae 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 7419
clementchadebec / benchmark_VAE
Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
benchmarking beta-vae comparison normalizing-flows pixel-cnn pytorch reproducibility reproducible-research vae vae-gan vae-implementation vae-pytorch variational-autoencoder vq-vae wasserstein-autoencoder
Language:Python 1962
YannDubs / disentangling-vae
Experiments for understanding disentanglement in VAE latent representations
beta-vae factor-vae vae variational-autoencoder unsupervised-learning celeba dsprites beta-tcvae disentangled-representations chairs-dataset mnist fashion-mnist representation-learning reproducible-research deep-learning pytorch disentanglement
Language:Python 832
1Konny / Beta-VAE
Pytorch implementation of β-VAE
beta-vae vae disentangled-representations unsupervised-learning celeba chairs-dataset dsprites
Language:Python 552
google-deepmind / dsprites-dataset
Dataset to assess the disentanglement properties of unsupervised learning methods
vae beta-vae disentanglement dataset dsprites
Language:Jupyter Notebook 515
EugenHotaj / pytorch-generative
Easy generative modeling in PyTorch
image-generation autoregressive-neural-networks pixelcnn pixelsnail image-gpt generative-models vq-vae paper-implementations vd-vae beta-vae variational-autoencoders pytorch-generative
Language:Python 438
1Konny / FactorVAE
Pytorch implementation of FactorVAE proposed in Disentangling by Factorising(http://arxiv.org/abs/1802.05983)
beta-vae celeba chairs-dataset disentangled-representations dsprites factor-vae factorvae unsupervised-learning vae
Language:Python 283
miyosuda / disentangled_vae
Replicating "Understanding disentangling in β-VAE"
vae beta-vae disentanglement dsprites
Language:Python 200
ml-tool-wear
tvhahn / ml-tool-wear
Anomaly detection on the UC Berkeley milling data set using a disentangled-variational-autoencoder (beta-VAE). Replication of results as described in article "Self-Supervised Learning for Tool Wear Monitoring with a Disentangled-Variational-Autoencoder"
deep-learning masc-thesis machinery-condition-monitoring tool-wear-monitoring anomaly-detection beta-vae tool-condition-monitoring advanced-manufacturing latent-spaces milling recall trend precision
Language:Jupyter Notebook 73
Kyushik / Generative-Model
Repository for implementation of generative models with Tensorflow 1.x
gan vae dcgan lsgan wgan wgan-gp cgan infogan cyclegan stargan aggan vq-vae beta-vae hologan generative-models raindrop-removal singan stylegan pggan
Language:Jupyter Notebook 71
1Konny / WAE-pytorch
wasserstein-autoencoder wae wasserstein-gan wgan vae beta-vae
Language:Python 66
marisancans / frame-predict-VAE-LSTM
Predicting image frames using autoencoder and LSTM
pytorch vae beta-vae opencv lstm seq2seq predict frame-predict
Language:Python 42
yuki3-18 / Topological-DVAE
An implementation of Denoising Variational AutoEncoder with Topological loss
pytorch beta-vae denoising-autoencoders topological-data-analysis
Language:Python 32
1Konny / idgan
Official PyTorch implementation on ID-GAN: High-Fidelity Synthesis with Disentangled Representation by Lee et al., 2020.
disentangled-representations beta-vae infogan generative-adversarial-network variational-autoencoder
Language:Python 30
mmrl / disent-and-gen
Code from the article: "The Role of Disentanglement in Generalisation" (ICLR, 2021).
generative-model psychology generalisation generalization pytorch disentanglement vae beta-vae deep-learning machine-learning variational-autoencoder
Language:Jupyter Notebook 22
Glaciohound / SCAN
Pytorch implementation of SCAN: Learning Hierarchical Compositional Visual Concepts, Higgins et al., ICLR 2018
machine-learning deep-learning computer-vision beta-vae vae disentan celeba scan
Language:Python 12
AloneTogetherY / disentangled-VAE
Disentangling the latent space of a VAE.
vae variational autoencoder generative models tensorflow keras beta-vae disentanglement
Language:Python 9
kundan2510 / vae_celeba
vae beta-vae celeba image-generation deep-learning
Language:Python 8
AxelNathanson / pytorch-Variational-Autoencoder
Implementation of VAE in pytorch.
pytorch vae beta-vae fashion-mnist
Language:Python 4
Bhavik-Ardeshna / pytorch-VAE
Variational Autoencoder and a Disentangled version (beta-VAE) implementation in PyTorch-Lightning
beta-vae generative-ai image-generation pytorch pytorch-lightning scratch-implementation variational-autoencoder
Language:Python 3
saashd / controlVAE
ML2 Project following ControlVAE: Tuning, Analytical Properties, and Performance Analysis
celeba disentangled-representations dsprites machine-learning mnist controlvae beta-vae vae vae-pytorch python pythorch variational-autoencoder
Language:Jupyter Notebook 3
sungsujaing / letter_digit_generator_VAE
generate arbitrary handwritten letter/digits based on the inputs
vae beta-vae tensorflow-keras conditional-vae emnist-dataset convolutional-cvae
Language:Jupyter Notebook 3
thomasjv799 / Beta-VAE-on-Animal-Face
Using Beta VAE to test on faces of animal
vae generative-model beta-vae
Language:Jupyter Notebook 2
JohanYe / Beta-VAE
Pytorch implementation of a simple beta vae on dsprites data
dsprites-data beta-vae dci dci-metric latent-traversals pytorch pytorch-implementation simple
Language:Python 1
msmrexe / pytorch-beta-vae-fashion-mnist
A PyTorch implementation of a β-Variational Autoencoder (β-VAE) for disentangled representation learning and image generation on the Fashion-MNIST dataset. This repository showcases how β-VAE can achieve disentanglement in its latent space, a crucial concept for interpretability in generative models.
beta-vae convolutional-neural-networks course-project disentangled-representations fashion-mnist frechet-inception-distance generative-models image-generation image-reconstruction latent-space-exploration reparameterization-trick university-project variational-autoencoder
Language:Python 1
evry-paris-saclay / 2023-m2cns-deepLearning-BetaVAE
beta-vae deep-learning variational-autoencoder
Language:Python 0
kumatheworld / fakeglyph
Fancy brand new letters with generative models
beta-vae gan image-generation pytorch vae
Language:Python 0
masashi-hatano / beta-vae
Image generation of MNIST via beta-VAE
beta-vae manifold-learning mnist
Language:Jupyter Notebook 0
Minitechy / betaVAE_dis
Investigating Disentanglement in beta-VAE within a Linear Gaussian Setting
beta-vae machine-learning nonlinear-optimization numerical-analysis
Language:Jupyter Notebook 0
Minitechy / betaVAE_recon
Augmenting Reconstruction Accuracy in beta-VAE Model through Linear Gaussian Framework
beta-vae machine-learning nonlinear-optimization numerical-analysis
Language:Jupyter Notebook 0
Minitechy / betaVAE_variants
beta-VAE variants trained using deep neural networks on the dSprites dataset with PyTorch
beta-vae disentanglement dsprites mig pytorch sap deep-neural-networks mutual-information
Language:Jupyter Notebook 0
suzuki-2001 / pytorch-proVLAE
Pytorch Implementation of proVLAE: progressive learning of variational ladder auto encoder
pytorch vae disentangled-representations beta-vae disentanglement imagenet mnist 3dshapes
Language:Python 0
Tristan-Amadei / DeepLearning-GenAI
Utilisation de modèles génératifs comme tâche prétexte pour pré-entrainement de DNN pour classification.
classification dbn dnn genrative-ai rbm beta-vae gan vae
Language:Jupyter Notebook 0
zombieTDV / CNN_and_Beta-VAE
CNN and Beta-VAE implementation. Only Numpy as the main library.
deep-learning automatic-differentiation backpropagation-algorithm beta-vae cnn optimization-algorithms numpy
Language:Python 0
k-karna / VAEs
Variational AutoEncoder (VAE) variants i.e., VAE, Beta-VAE, Dirichlet-VAE, VQ-VAE implemented on MNIST & CelebA with PyTorch
beta-vae dirichlet-vae vae variational-autoencoder vq-vae
Language:Jupyter Notebook
Sh-31 / VAE-MNIST
Implementation of Variational Autoencoder (VAE) for the MNIST dataset, supporting both fully connected (FNN) and convolutional (CNN) architectures. It includes training, evaluation, and empirical analysis.
beta-vae mnist pytorch transpose-convolution vae variational-autoencoder
Language:Jupyter Notebook

beta-vae

AntixK / PyTorch-VAE

clementchadebec / benchmark_VAE

YannDubs / disentangling-vae

1Konny / Beta-VAE

google-deepmind / dsprites-dataset

EugenHotaj / pytorch-generative

1Konny / FactorVAE

miyosuda / disentangled_vae

tvhahn / ml-tool-wear

Kyushik / Generative-Model

1Konny / WAE-pytorch

marisancans / frame-predict-VAE-LSTM

yuki3-18 / Topological-DVAE

1Konny / idgan

mmrl / disent-and-gen

Glaciohound / SCAN

AloneTogetherY / disentangled-VAE

kundan2510 / vae_celeba

AxelNathanson / pytorch-Variational-Autoencoder

Bhavik-Ardeshna / pytorch-VAE

saashd / controlVAE

sungsujaing / letter_digit_generator_VAE

thomasjv799 / Beta-VAE-on-Animal-Face

JohanYe / Beta-VAE

msmrexe / pytorch-beta-vae-fashion-mnist

evry-paris-saclay / 2023-m2cns-deepLearning-BetaVAE

kumatheworld / fakeglyph

masashi-hatano / beta-vae

Minitechy / betaVAE_dis

Minitechy / betaVAE_recon

Minitechy / betaVAE_variants

suzuki-2001 / pytorch-proVLAE

Tristan-Amadei / DeepLearning-GenAI

zombieTDV / CNN_and_Beta-VAE

k-karna / VAEs

Sh-31 / VAE-MNIST