@inproceedings{f7f1ad70a3524a179696bd0bf222d286,
title = "Fourier transformation autoencoders for anomaly detection",
abstract = "Anomaly detection is a challenging problem, mainly due to the lack of a sufficient set of abnormal samples that represents every possible anomaly. Therefore unsupervised methods are employed to model normality and anomaly is detected as an outlier to such models. This paper introduces Fourier Trans-forms into AutoEncoders to demonstrate how the inclusion of a frequency domain presents less noisy features for a deep learning network to detect anomalies. Comparing our results to the state of the art on a variety of datasets, we show how the proposed method can provide competitive results.",
keywords = "Computer science and informatics",
author = "Demetris Lappas and Vasileios Argyriou and Dimitrios Makris",
note = "Organising Body: Institute of Electrical and Electronics Engineers; 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ; Conference date: 06-06-2021 Through 11-06-2021",
year = "2021",
month = may,
day = "13",
doi = "10.1109/ICASSP39728.2021.9415010",
language = "English",
isbn = "9781728176062",
series = "International Conference on Acoustics, Speech, and Signal Processing (ICASSP)",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "1475--1479",
booktitle = "ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",
address = "United States",
}