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Autoencoder ○꠹｜Definition｜1st｜20251119205401-00-⌔
Autoencoder
An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning). An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation. The autoencoder learns an efficient representation (encoding) for a set of data, typically for dimensionality reduction, to generate lower-dimensional embeddings for subsequent use by other machine learning algorithms.1
Variants exist which aim to make the learned representations assume useful properties.2 Examples are regularized autoencoders (sparse, denoising and contractive autoencoders), which are effective in learning representations for subsequent classification tasks,3 and variational autoencoders, which can be used as generative models.4 Autoencoders are applied to many problems, including facial recognition,5 feature detection,6 anomaly detection, and learning the meaning of words.78 In terms of data synthesis, autoencoders can also be used to randomly generate new data that is similar to the input (training) data.6
Printed 2026-06-28.
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Link to original Footnotes
Bank, Dor; Koenigstein, Noam; Giryes, Raja (2023). “Autoencoders”. In Rokach, Lior; Maimon, Oded; Shmueli, Erez (eds.). Machine learning for data science handbook. pp. 353–374. doi:10.1007/978-3-031-24628-9_16. ISBN 978-3-031-24627-2. ↩
Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron (2016). Deep Learning. MIT Press. ISBN 978-0262035613. ↩
Vincent, Pascal; Larochelle, Hugo (2010). “Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion”. Journal of Machine Learning Research. 11: 3371–3408. ↩
Welling, Max; Kingma, Diederik P. (2019). “An Introduction to Variational Autoencoders”. Foundations and Trends in Machine Learning. 12 (4): 307–392. arXiv:1906.02691. Bibcode:2019arXiv190602691K. doi:10.1561/2200000056. S2CID 174802445. ↩
Hinton GE, Krizhevsky A, Wang SD. Transforming auto-encoders. In International Conference on Artificial Neural Networks 2011 Jun 14 (pp. 44-51). Springer, Berlin, Heidelberg. ↩
Géron, Aurélien (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. Canada: O’Reilly Media, Inc. pp. 739–740. ↩ ↩2
Liou, Cheng-Yuan; Huang, Jau-Chi; Yang, Wen-Chie (2008). “Modeling word perception using the Elman network”. Neurocomputing. 71 (16–18): 3150. doi:10.1016/j.neucom.2008.04.030. ↩
Liou, Cheng-Yuan; Cheng, Wei-Chen; Liou, Jiun-Wei; Liou, Daw-Ran (2014). “Autoencoder for words”. Neurocomputing. 139: 84–96. doi:10.1016/j.neucom.2013.09.055. ↩
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