Auto Encoders in PyTorch

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

Tensorflow 2.0 implementation of Adversarial Autoencoders

Additional resources for an overview on autoencoders

Stacked Denoising and Variational Autoencoder implementation for MNIST dataset

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

image reconstruction with pytorch

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

Different models of autoencoders: shallow, deep, convolutional, VAE, IWAE, DVAE, DIWAE

Multi Class Classification and Autoencoder for MNIST Dataset using Multi Layer Feed Forward Neural Net implemented from scratch

This repository contains Autoencoders, Variational Autoencoders and GANS-Unsupervised Models developed for MNIST Dataset in Tensorflow and PyTorch.

Pythorch implementation of Winner-Take-All Autoencoder

Autoencoder implementations and experiments with MNIST. MSU DL course.

My DATAML200 - Pattern Recognition And Machine Learning course implementations.

Audio encoder for reconstruct, denoise image or audio spectrogram

Autoencoder - Variational Autoencoder - Anomaly detection - using PyTorch

Variational Autoencoder (VAE) trained on MNIST