autoencoder-mnist
There are 86 repositories under autoencoder-mnist topic.
ReyhaneAskari/pytorch_experiments
Auto Encoders in PyTorch
abhisheksambyal/Autoencoders-using-Pytorch-Medical-Imaging
Medical Imaging, Denoising Autoencoder, Sparse Denoising Autoencoder (SDAE) End-to-end and Layer Wise Pretraining
nmeripo/Reducing-the-Dimensionality-of-Data-with-Neural-Networks
Implementation of G. E. Hinton and R. R. Salakhutdinov's Reducing the Dimensionality of Data with Neural Networks (Tensorflow)
eugenet12/pytorch-rbm-autoencoder
Pytorch implementation of an autoencoder built from pre-trained Restricted Boltzmann Machines (RBMs)
Mind-the-Pineapple/adversarial-autoencoder
Tensorflow 2.0 implementation of Adversarial Autoencoders
satolab12/anomaly-detection-using-autoencoder-PyTorch
encoder-decoder based anomaly detection method
fdavidcl/ae-review-resources
Additional resources for an overview on autoencoders
arunarn2/AutoEncoder
Stacked Denoising and Variational Autoencoder implementation for MNIST dataset
codeperfectplus/autoEncoders
Deep convolutional autoencoder for image denoising
captain-pool/MNIST_AutoEncoder
AutoEncoder on MNIST Digit
sarthak268/Deep_Neural_Networks
This repository contains Pytorch files that implement Basic Neural Networks for different datasets.
LorenzoValente3/Autoencoder-for-FPGA
Autoencoder model for FPGA implementation using hls4ml. Repository for Applied Electronics Project.
ariss95/image_reconstructon_examples
image reconstruction with pytorch
braininahat/CVIP-CSE573
UB Computer Vision
MatejBabis/TensorFlow2Autoencoder
Basic deep fully-connected autoencoder in TensorFlow 2
samridhishree/Deeplearning-Models
Deep learning models in Python
spyros-briakos/AutoEncoder-and-Classifier-of-MNIST-images
Implementation of an Auto-Encoder and Classifier so as to classify images from MNIST dataset.
A-Raafat/Classifiers-and-MNIST-Data
Extracting features using PCA, DCT, Centroid features and Auto encoder of 1 hidden-layer then classifying using K-means, GMM, SVM
Gauravshahare/ADVERSIAL_AUTOENCODER
Implementation of paper (https://arxiv.org/abs/1511.05644) for my own research
LorenzoValente3/Deep-Learning-Models
Keras implementation of Deep Learning Models applied to the MNIST and Polynomial datasets. Repository for the Software and Computing for Nuclear and Subnuclear Physics Project.
Nishant2018/AutoEncoder-Generative-AI-MNIST
Autoencoders are a type of neural network used for unsupervised learning. In unsupervised learning, the model learns patterns from the data without using labeled outcomes. The goal is to find the underlying structure or representation of the data.
priyavrat-misra/image-compression-cae
Image compression using Convolutional Autoencoders.
saurabhkemekar/Denoising-Autoencoders
This project contains implementation of denoising autoencoder
07Agarg/Unsupervised-Learning
This repository contains Autoencoders, Variational Autoencoders and GANS-Unsupervised Models developed for MNIST Dataset in Tensorflow and PyTorch.
Bilpapster/NNs-playground
Neural Networks source code for image classification and reconstruction
favalos/autoencoder-mxnet-scala
Simple implementation of Autoencoder with mxnet and scala.
martinetoering/Visual-Representation-Learning-Tutorials
Project materials for teaching bachelor students about fundamentals on Deep learning, PyTorch, ConvNets & Autoencoder (January, 2021).
Ninoko/Autoencoders
Different models of autoencoders: shallow, deep, convolutional, VAE, IWAE, DVAE, DIWAE
princeGedeon/Auto_encoder_with_tensorflow
Auto encoder pour la reduction de dimensionnalité d'un dataset en 2D ou en 3D pour mieux visionner et aussi pour la suppression de bruit sur des données images (Cas MNIST)
sayannath/Noise-Reduction-using-Auto-Encoders
Noise Reduction of Images using Auto Encoders.
shivchander/mnist-multilayer-feedforward
Multi Class Classification and Autoencoder for MNIST Dataset using Multi Layer Feed Forward Neural Net implemented from scratch
mayankmittal29/TensorTinker_Statistical_Methods_in_AI
This repository contains implementations of various machine learning algorithms from scratch, including Multi-Layer Perceptron (MLP), Gaussian Mixture Models (GMM), Principal Component Analysis (PCA), Autoencoders, and Variational Autoencoders.
shadoisper/k-sparse-autoencoder
A K SPARSE AUTOENCODER OR YOU CAN SAY TOP K AUTOENCODER WITH FASHION MNIST DATA SET FOR DECONSTRUCTION AND RECONSTRUCTION WITH RETAINING MOST OF THE DATA.