399 Open source projects that are alternatives of or similar to tensorflow-mnist-AAE

Tensorflow implementation of conditional variational auto-encoder for MNIST

Tensorflow 2.0 implementation of Adversarial Autoencoders

Tensorflow implementation of variational auto-encoder for MNIST

Assessing Generative Models via Precision and Recall (official repository)

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

A curated list of dedicated resources and applications

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

Minimalist implementation of VQ-VAE in Pytorch

Advanced Deep Learning with Keras, published by Packt

Deep Learning sample programs using PyTorch in C++

Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN

State Representation Learning (SRL) zoo with PyTorch - Part of S-RL Toolbox

Neural network library written in C and Javascript

Experiments for understanding disentanglement in VAE latent representations

Variational Autoencoder and Conditional Variational Autoencoder on MNIST in PyTorch

Tensorflow Implementation of Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation (NAACL 2019).

Collection of generative models in Tensorflow

Playing with MNIST. Machine Learning. Generative Models.

A look at some simple autoencoders for the Cifar10 dataset, including a denoising autoencoder. Python code included.

Learning about Haskell with Variational Autoencoders

텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다

Tensorflow implementation of Adversarial Autoencoders

An implementation of a Variational-Autoencoder using the Gumbel-Softmax reparametrization trick in TensorFlow (tested on r1.5 CPU and GPU) in ICLR 2017.

Comparison of Generative Models in Tensorflow

Generative Probabilistic Novelty Detection with Adversarial Autoencoders

Integrated Cell project implemented in pytorch

🌐 Deep Embedded Self-Organizing Map: Joint Representation Learning and Self-Organization

Keras implementations of three language models: character-level RNN, word-level RNN and Sentence VAE (Bowman, Vilnis et al 2016).

手写数字识别。利用Tensorflow和MNIST实现的一个简单的手写数字识别程序。

Keras MNIST for Handwriting Detection

CNN classification model built in Keras used for Digit Recognizer task on Kaggle (https://www.kaggle.com/c/digit-recognizer)

No description or website provided.

No description or website provided.

Predict handwritten digits with CoreML

UKF over generalized manifold in Matlab

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

Official PyTorch implementation of "Deep State Space Models for Nonlinear System Identification", 2020.

Using Autoencoders for classification as unsupervised machine learning algorithms with Deep Learning.

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

DeepGTT: Learning Travel Time Distributions with Deep Generative Model

EfficientMORL (ICML'21)

Simple Implementation of many GAN models with PyTorch.

Data Science and Matrix Optimization course

A scratch implementation of Convolutional Neural Network in Python using only numpy and validated over CIFAR-10 & MNIST Dataset

Çeşitli kütüphaneler kullanılarak Türkçe kod açıklamalarıyla TEMEL SEVİYEDE pratik derin öğrenme uygulamaları.

Implementation of a convolutional Variational-Autoencoder model in pytorch.

This is the official implementation for the paper 'AutoEncoder by Forest'

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

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

Lingvo

MNIST with TensorFlow Lite on Android

EANN: event-adversarial neural networks for multi-modal fake news detection

Generative Adversarial Network for MNIST with tensorflow

Collection of generative models in Pytorch version.

Code accompanying ISMIR'19 paper titled "Learning to Traverse Latent Spaces for Musical Score Inpaintning"

Implementation of a digit recognition using my Neural Network with the MNIST data set.

MNIST classification using Convolutional NeuralNetwork. Various techniques such as data augmentation, dropout, batchnormalization, etc are implemented.

Generalised and highly customisable, hybrid-parallelism, database based, deep learning framework.

Non-negative Positive-Unlabeled (nnPU) and unbiased Positive-Unlabeled (uPU) learning reproductive code on MNIST and CIFAR10