Convolutional autoencoders

Articles & Tutorials

Intro

Building Autoencoders in Keras. In this tutorial, we will answer some common questions about autoencoders, and we will cover code examples of the following models:

• a simple autoencoder based on a fully-connected layer
• a sparse autoencoder
• a deep fully-connected autoencoder
• a deep convolutional autoencoder
• an image denoising model
• a sequence-to-sequence autoencoder
• a variational autoencoder

08_AE.pdf Slides to get the basic idea of AE.

Types of convolutions

https://towardsdatascience.com/types-of-convolutions-in-deep-learning-717013397f4d

Variational Autoencoders

Variational Autoencoders Explained. After reading this post, you'll be equipped with the theoretical understanding of the inner workings of VAE, as well as being able to implement one yourself.

Literature overview

Unsupervised learning using Convolutional Autoencoders

Pre-Training CNNs Using Convolutional Autoencoders

Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction

Extension to CAE

DEEP AUTOENCODING GAUSSIAN MIXTURE MODEL FOR UNSUPERVISED ANOMALY DETECTION

Edit: This paper has good information about the autoencders: autoencoder.pdf