Auto Encoders in PyTorch
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Updated
Jan 29, 2018 - Python
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
Multi Class Classification and Autoencoder for MNIST Dataset using Multi Layer Feed Forward Neural Net implemented from scratch
Different models of autoencoders: shallow, deep, convolutional, VAE, IWAE, DVAE, DIWAE
This repository contains Autoencoders, Variational Autoencoders and GANS-Unsupervised Models developed for MNIST Dataset in Tensorflow and PyTorch.
Autoencoder - Variational Autoencoder - Anomaly detection - using PyTorch
Autoencoders (AE) are a family of neural networks for which the input is the same as the output. They work by compressing the input into a latent-space representation, and then reconstructing the output from this representation.
Autoencoder implementations and experiments with MNIST. MSU DL course.
Comparison between a linear and convolutional autoencoder.
Variational Autoencoder (VAE) trained on MNIST
Audio encoder for reconstruct, denoise image or audio spectrogram
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