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.
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CITATION STYLE
APA
Bank, D., Koenigstein, N., & Giryes, R. (2023). Autoencoders. In Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, Third Edition (pp. 353–374). Springer International Publishing. https://doi.org/10.1007/978-3-031-24628-9_16
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