Autoencoders

Dor Bank; Noam Koenigstein; Raja Giryes

doi:10.1007/978-3-031-24628-9_16

Autoencoders

Dor Bank, Noam Koenigstein, Raja Giryes

Tel Aviv University

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

An autoencoder is a specific type of a neural network, which is mainly designed to encode the input into a compressed and meaningful representation and then decode it back such that the reconstructed input is similar as possible to the original one. This chapter surveys the different types of autoencoders that are mainly used today. It also describes various applications and use-cases of autoencoders.

Original language	English
Title of host publication	Machine Learning for Data Science Handbook
Subtitle of host publication	Data Mining and Knowledge Discovery Handbook, Third Edition
Pages	353-374
Number of pages	22
ISBN (Electronic)	9783031246289
DOIs	https://doi.org/10.1007/978-3-031-24628-9_16
State	Published - 1 Jan 2023

All Science Journal Classification (ASJC) codes

General Computer Science
General Mathematics

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10.1007/978-3-031-24628-9_16

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Autoencoders

Abstract

All Science Journal Classification (ASJC) codes

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