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

• Robust, Deep and Inductive Anomaly Detection "This paper addresses both issues in a single model, the robust autoencoder. This method learns a nonlinear subspace that captures the majority of data points, while allowing for some data to have arbitrary corruption. The model is simple to train and leverages recent advances in the optimisation of deep neural networks. Experiments on a range of real-world datasets highlight the model’s effectiveness." Might be helpfull for improving CAE

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