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Auto-Encoders in Deep Learning—A Review with New Perspectives

Author

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  • Shuangshuang Chen

    (Jiangsu Provincial Key Constructive Laboratory for Big Data of Psychology and Cognitive Science, Yancheng Teachers University, Yancheng 224002, China
    College of Information Engineering, Yancheng Teachers University, Yancheng 224002, China)

  • Wei Guo

    (College of Information Engineering, Yancheng Teachers University, Yancheng 224002, China)

Abstract

Deep learning, which is a subfield of machine learning, has opened a new era for the development of neural networks. The auto-encoder is a key component of deep structure, which can be used to realize transfer learning and plays an important role in both unsupervised learning and non-linear feature extraction. By highlighting the contributions and challenges of recent research papers, this work aims to review state-of-the-art auto-encoder algorithms. Firstly, we introduce the basic auto-encoder as well as its basic concept and structure. Secondly, we present a comprehensive summarization of different variants of the auto-encoder. Thirdly, we analyze and study auto-encoders from three different perspectives. We also discuss the relationships between auto-encoders, shallow models and other deep learning models. The auto-encoder and its variants have successfully been applied in a wide range of fields, such as pattern recognition, computer vision, data generation, recommender systems, etc. Then, we focus on the available toolkits for auto-encoders. Finally, this paper summarizes the future trends and challenges in designing and training auto-encoders. We hope that this survey will provide a good reference when using and designing AE models.

Suggested Citation

  • Shuangshuang Chen & Wei Guo, 2023. "Auto-Encoders in Deep Learning—A Review with New Perspectives," Mathematics, MDPI, vol. 11(8), pages 1-54, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:8:p:1777-:d:1118629
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    References listed on IDEAS

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    1. Shuangshuang Chen & Huiyi Liu & Xiaoqin Zeng & Subin Qian & Jianjiang Yu & Wei Guo, 2017. "Image Classification Based on Convolutional Denoising Sparse Autoencoder," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-16, November.
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