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)

encoder-decoder based anomaly detection method

Tensorflow 2.0 implementation of Adversarial Autoencoders

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

UB Computer Vision

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

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.

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.