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Comparison and Analysis of Various Autoencoders

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Emerging Technologies in Data Mining and Information Security

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1348))

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Abstract

The autoencoder is family of deep neutral network which learns to reconstruct its input. It has three main parts encoder, code, and decoder. Autoencoders are effective unsupervised learning method which encode an input into a lower dimensional representation. This representation input consist of input as features are useful for image processing applications. The size of hidden representation is lesser then the original image that’s under complete autoencoder. If the size is greater than the hidden representation that is over complete autoencoder. This paper compares and evaluates many architectures of autoencoders model.

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Authors and Affiliations

  1. Karnataka State Akkamahadevi Women’s University, Vijayapura, India

    Aziz Makandar & Kanchan Wangi

Authors
  1. Aziz Makandar
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  2. Kanchan Wangi
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Corresponding author

Correspondence to Kanchan Wangi .

Editor information

Editors and Affiliations

  1. Department of Computer and System Sciences, Visva-Bharati University, Santiniketan, India

    Paramartha Dutta

  2. Department of Computer Application and Science, Institute of Engineering and Management, Kolkata, West Bengal, India

    Abhishek Bhattacharya

  3. Institute of Engineering and Management, Kolkata, India

    Soumi Dutta

  4. Department of Electronic Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Wen-Cheng Lai

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Makandar, A., Wangi, K. (2023). Comparison and Analysis of Various Autoencoders. In: Dutta, P., Bhattacharya, A., Dutta, S., Lai, WC. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 1348. Springer, Singapore. https://doi.org/10.1007/978-981-19-4676-9_6

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