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Autoencoders

In: Machine Learning for Data Science Handbook

Author

Listed:
  • Dor Bank

    (Tel Aviv University, School of Electrical Engineering)

  • Noam Koenigstein

    (Tel Aviv University, Department of Industrial Engineering, Faculty of Engineering)

  • Raja Giryes

    (Tel Aviv University, School of Electrical Engineering)

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.

Suggested Citation

  • Dor Bank & Noam Koenigstein & Raja Giryes, 2023. "Autoencoders," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 353-374, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-24628-9_16
    DOI: 10.1007/978-3-031-24628-9_16
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