Autoencoders: Overview of Research and Applications

Since the early days of machine learning, it has been attempted to learn good representations of data in an unsupervised manner. The hypothesis underlying this effort is that disentangled representations translate well to downstream supervised tasks. For example, if a human is told that a Tesla is a car and he has a good representation of what a car looks like, he can probably recognize a photo of a Tesla among photos of houses without ever seeing a Tesla.

Most early representation learning ideas revolve around linear models such as factor…