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=== Anomaly Detection ===
Another field of application for autoencoders is [[anomaly detection]].<ref>Sakurada, M., & Yairi, T. (2014, December). Anomaly detection using autoencoders with nonlinear dimensionality reduction. In ''Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis'' (p. 4). ACM.</ref><ref name=":8">An, J., & Cho, S. (2015). Variational autoencoder based anomaly detection using reconstruction probability. ''Special Lecture on IE'', ''2'', 1-18.</ref><ref>Zhou, C., & Paffenroth, R. C. (2017, August). Anomaly detection with robust deep autoencoders. In ''Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining'' (pp. 665-674). ACM.</ref><ref>Ribeiro, M., Lazzaretti, A. E., & Lopes, H. S. (2018). A study of deep convolutional auto-encoders for anomaly detection in videos. ''Pattern Recognition Letters'', ''105'', 13-22.</ref><ref>{{Cite journal|last=Zavrak|first=Sultan|last2=Iskefiyeli|first2=Murat|date=2020|title=ANOMALY-BASED INTRUSION DETECTION FROM NETWORK FLOW FEATURES USING VARIATIONAL AUTOENCODER|url=https://ieeexplore.ieee.org/document/9113298/|journal=IEEE Access|pages=1–1|doi=10.1109/ACCESS.2020.3001350|issn=2169-3536|doi-access=free}}</ref> By learning to replicate the most salient features in the training data under some of the constraints described previously, the model is encouraged to learn how to precisely reproduce the most frequent characteristics of the observations. When facing anomalies, the model should worsen its reconstruction performance. In most cases, only data with normal instances are used to train the autoencoder; in others, the frequency of anomalies is so small compared to the whole population of observations, that its contribution to the representation learnt by the model could be ignored. After training, the autoencoder will reconstruct normal data very well, while failing to do so with anomaly data which the autoencoder has not encountered.<ref name=":8" /> Reconstruction error of a data point, which is the error between the original data point and its low dimensional reconstruction, is used as an anomaly score to detect anomalies.<ref name=":8" />
=== Image Processing ===
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