Auto Encoders in PyTorch

Medical Imaging, Denoising Autoencoder, Sparse Denoising Autoencoder (SDAE) End-to-end and Layer Wise Pretraining

Implementation of G. E. Hinton and R. R. Salakhutdinov's Reducing the Dimensionality of Data with Neural Networks (Tensorflow)

Pytorch implementation of an autoencoder built from pre-trained Restricted Boltzmann Machines (RBMs)

Tensorflow 2.0 implementation of Adversarial Autoencoders

encoder-decoder based anomaly detection method

Additional resources for an overview on autoencoders

Stacked Denoising and Variational Autoencoder implementation for MNIST dataset

Deep convolutional autoencoder for image denoising

AutoEncoder on MNIST Digit

This repository contains Pytorch files that implement Basic Neural Networks for different datasets.

Autoencoder model for FPGA implementation using hls4ml. Repository for Applied Electronics Project.

image reconstruction with pytorch

Implementation of an Auto-Encoder and Classifier so as to classify images from MNIST dataset.

UB Computer Vision

Basic deep fully-connected autoencoder in TensorFlow 2

Deep learning models in Python

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.

Extracting features using PCA, DCT, Centroid features and Auto encoder of 1 hidden-layer then classifying using K-means, GMM, SVM