AutoClass integrates a classifier to a regular autoencoder, as to fully reconstruct scRNA-Seq data. a AutoClass consists a regular autoencoder and a classifier branch from the bottleneck layer.

An autoencoder learns to predict its input ... However, this isn't a principled approach because binary cross entropy loss is intended for binary classification problems. Additionally, binary cross ...

Facebook has combined an “adversarial autoencoder” and a “trained-face classifier”. An autoencoder is an artificial neural network that learns a representation for a set of data unsupervised.

This is called dimensionality reduction. The two most common techniques for dimensionality reduction are using PCA (principal component analysis) and using a neural autoencoder. This article explains ...