Autoencoders' example uses augment data for machine learning Posted on : Aug 01 - 2020

Autoencoders are neural networks that serve machine learning models -- from denoising to dimensionality reduction. Seven use cases explore the practical application of autoencoder technology.

Developers frequently turn to autoencoders to organize data for machine learning algorithms to improve the efficiency and accuracy of algorithms with less effort from data scientists.

Data scientists can add autoencoders as additional tools to applications which require data denoising, nonlinear dimensionality reduction, sequence-to-sequence prediction and feature extraction. Autoencoders have a special advantage over classic machine learning techniques like principal component analysis for dimensionality reduction in that they can represent data as nonlinear representations -- and work particularly well in feature extraction.

Autoencoders 101

Until recently, the study of autoencoders had primarily been an academic pursuit, said Nathan White, lead consultant at AIM Consulting. However, there are now many applications where machine learning practitioners should look to autoencoders as their tool of choice. But before diving into the top use cases, here's a brief look into autoencoder technology.

An autoencoder consists of a pair of deep learning networks, an encoder and decoder. The encoder learns an efficient way of encoding input into a smaller dense representation, called the bottleneck layer. After training, the decoder converts this representation back to the original input.

"The essential principle of an autoencoder is to distill the input into the smallest amount of data necessary to then reconstruct that original input with as little difference as possible between the input and the output," said Pat Ryan, executive vice president of enterprise architecture at digital tech consultancy SPR.

The value of the autoencoder is that it removes noise from the input signal, leaving only a high-value representation of the input. With this, machine learning algorithms can perform better because the algorithms are able to learn the patterns in the data from a smaller set of a high-value input, Ryan said.

Autoencoders, unsupervised neural networks, are proving useful in machine learning domains with extremely high data dimensionality and nonlinear properties such as video, image or voice applications.

Advantages of autoencoders

One important characteristic of autoencoders is that they can work in an unsupervised manner, which eliminates the need to label the training data, whether by hand or artificially.

"[Autoencoders] are unique in that they leverage the benefits of supervised learning without the need for manual annotation, since inputs and outputs of the network are the same," said Sriram Narasimhan, vice president for artificial intelligence and analytics at IT service firm Cognizant. View More