vae-implementation
There are 1 repository under vae-implementation topic.
AntixK / PyTorch-VAE
A Collection of Variational Autoencoders (VAE) in PyTorch.
Language:Python 7419clementchadebec / benchmark_VAE
Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
Language:Python 1962is0383kk / Pytorch_VAE-GMM
Implementation of mutual learning model between VAE and GMM.
Language:Python 28meiyulee / 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.
Language:C++ 25Dirichlet-Variational Auto-Encoder by PyTorch
Language:Jupyter Notebook 23insdout / GMVAE-pytorch
Pytorch implementation of Gaussian Mixture Variational Autoencoder GMVAE
Language:Python 20julian-carpenter / beta-TCVAE
Tensorflow 2.x implementation of the beta-TCVAE (arXiv:1802.04942).
Language:Python 16RGenDiff / open-litevae
Implementation of LiteVAE
Language:Python 10vsimkus / pmr2024-vae
An official repository for a VAE tutorial of Probabilistic Modelling and Reasoning - a University of Edinburgh master's course.
Language:Jupyter Notebook 9Symbol emergence using Variational Auto-Encoder and Gaussian Mixture Model (Inter-GMM-VAE)~VAEを活用した実画像からの記号創発~
Language:Python 8HariKrishnan06082k / 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.
Language:Jupyter Notebook 6keya-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
Language:Python 5Baukebrenninkmeijer / Variational-Autoencoder-Pytorch
Implementation of the variational autoencoder with PyTorch and Fastai
Language:Jupyter Notebook 4ioangatop / DeepGenerativeModels
Variational Auto Encoders (VAEs), Generative Adversarial Networks (GANs) and Generative Normalizing Flows (NFs) and are the most famous and powerful deep generative models.
Language:Python 4raulorteg / SmileCVAE
Implementation of CVAE. Trained CVAE on faces from UTKFace Dataset to produce synthetic faces with a given degree of happiness/smileyness.
Language:Python 4ayulockin / deepgenerativemodeling
Towards Generative Modeling from (variational) Autoencoder to DCGAN
Language:Jupyter Notebook 3shreyansh26 / Sentence-VAE
A re-implementation of the Sentence VAE paper, Generating Sentences from a Continuous Space
Language:Python 3ayseirmak / 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.
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TimoFlesch / Autoencoders
Autoencoders (Standard, Convolutional, Variational), implemented in tensorflow
Language:Python 2unkosan / RaptGen-UI
The GUI for RaptGen developed with React and FastAPI
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DelTA-Lab-IITK / PDUN
Probabilistic framework for solving Visual Dialog
kleinzcy / Variational-AutoEncoder
VAE and CVAE pytorch implement based on MNIST
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nandiniigarg / ColorVAE
ColorVAE is a Vanilla Auto Encoder (V.A.E.) which can be used to add colours to black and white images.
Language:Jupyter Notebook 1quyongkeomut / Variational-Autoencoder
Implementation of VAEs (Variational Autoencoders) with PyTorch.
Language:Python 1Resh-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.
Language:Jupyter Notebook 1Shreshth-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
Language:Jupyter Notebook 1UW-CIA / Models_at_edge
Running VAEs on mobile and IOT devices using TFLite.
Language:Jupyter Notebook 1Carmoruda / latent-cats
PyTorch-based pipeline that trains a convolutional variational autoencoder on cat images, optionally tunes hyperparameters with Ray Tune, and samples new images by fitting a Gaussian Mixture Model in the latent space.
Language:Python 0mode1990 / 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
Language:Jupyter Notebook 0sathya-ml / multimodal-vrnn-vae
A PyTorch implementation of multimodal VRNN and VAE.
Language:Python 0MavAayush / mrd-detection-flow
Unsupervised MRD detection in flow cytometry data using Variational AutoEncoder (VAE) and Gaussian Mixture Model (GMM).
Language:Jupyter NotebookMehrab404 / AstroVAE-Variational-Autoencoder-for-Cosmological-Data-Compression
AstroVAE uses a Variational Autoencoder (VAE) to compress high-dimensional data from CAMELS cosmological simulations. It creates a compact, meaningful latent-space representation of complex astrophysical data, proving more effective at preserving critical scientific information than traditional methods.
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