687 Open source projects that are alternatives of or similar to VAE-Gumbel-Softmax

Keras implementation of a Variational Auto Encoder with a Concrete Latent Distribution

Tensorflow implementation of variational auto-encoder for MNIST

Collection of generative models in Tensorflow

Experiments for understanding disentanglement in VAE latent representations

Tensorflow implementation of conditional variational auto-encoder for MNIST

Variational Autoencoder and Conditional Variational Autoencoder on MNIST in PyTorch

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.

Pytorch implementation of Hyperspherical Variational Auto-Encoders

PyTorch implementation of "Auto-Encoding Variational Bayes"

Implemented Variational Autoencoder generative model in Keras for image generation and its latent space visualization on MNIST and CIFAR10 datasets

Tensorflow implementation of Hyperspherical Variational Auto-Encoders

Invariant representation learning from imaging and spectral data

VAE with RealNVP prior and Super-Resolution VAE in PyTorch. Code release for https://arxiv.org/abs/2006.05218.

Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)

This repository contains model-free deep reinforcement learning algorithms implemented in Pytorch

Example projects I completed to understand Deep Learning techniques with Tensorflow. Please note that I do no longer maintain this repository.

A Collection of Variational Autoencoders (VAE) in PyTorch.

Official implementation of the paper "Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders" (CVPR 2019)

A tensorflow implementation of "Generating Sentences from a Continuous Space"

Learning about Haskell with Variational Autoencoders

Comparison of Generative Models in Tensorflow

Machine Learning Toolkit for Extreme Scale (MaTEx)

Minimalist implementation of VQ-VAE in Pytorch

No description or website provided.

Tensorflow Implementation of the paper [Neural Discrete Representation Learning](https://arxiv.org/abs/1711.00937) (VQ-VAE).

Playing with MNIST. Machine Learning. Generative Models.

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

Assessing Generative Models via Precision and Recall (official repository)

music generation with a classifying variational autoencoder (VAE) and LSTM

Implementation of a convolutional Variational-Autoencoder model in pytorch.

でぃーぷらーにんぐを無限にやってディープラーニングでDeepLearningするための実装CheatSheet

A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.

Protein function prediction using a variational autoencoder

Android TensorFlow MachineLearning MNIST Example (Building Model with TensorFlow for Android)

A Tensorflow implementation of a Variational Autoencoder for the deep learning course at the University of Southern California (USC).

Code for "MojiTalk: Generating Emotional Responses at Scale" https://arxiv.org/abs/1711.04090

A CNN Variational Autoencoder (CNN-VAE) implemented in PyTorch

Handle class imbalance intelligently by using variational auto-encoders to generate synthetic observations of your minority class.

Stochastic Adversarial Video Prediction

[CVPR 2021 Oral] Official PyTorch implementation of Soft-IntroVAE from the paper "Soft-IntroVAE: Analyzing and Improving Introspective Variational Autoencoders"

Tensorflow implementation of adversarial auto-encoder for MNIST

The implementation of Relativistic average GAN with Keras

Deep generative models implemented with TensorFlow 2.0: eg. Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), Deep Boltzmann Machine (DBM), Convolutional Variational Auto-Encoder (CVAE), Convolutional Generative Adversarial Network (CGAN)

IPCCC 2018: Robust and Unsupervised KPI Anomaly Detection Based on Conditional Variational Autoencoder

Text Variational Autoencoder inspired by the paper 'Generating Sentences from a Continuous Space' Bowman et al. https://arxiv.org/abs/1511.06349

A small convolution neural network deep learning framework implemented in c++.

CapsNet (Capsules Net) in Geoffrey E Hinton paper "Dynamic Routing Between Capsules" - State Of the Art

Interactive exploration of MNIST variational autoencoder latent space with React and tensorflow.js.

Comparing FC VAE / FCN VAE / PCA / UMAP on MNIST / FMNIST

Implementation of key concepts of neuralnetwork via numpy

Shared Latent Space VAE's

Focal Loss of multi-classification in tensorflow

An optimizer that trains as fast as Adam and as good as SGD in Tensorflow

Neural nets for MNIST classification, simple single layer NN, 5 layer FC NN and convolutional neural networks with different architectures

Official implementation of "Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform", ICCV 2021

A PyTorch implementation of "Multimodal Generative Models for Scalable Weakly-Supervised Learning" (https://arxiv.org/abs/1802.05335)

Optical character recognition Using Deep Learning

Website for "A survey of learning to hash for Computer Vision" https://learning2hash.github.io