2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDCI-4.5
Paper Title FOURIER TRANSFORMATION AUTOENCODERS FOR ANOMALY DETECTION
Authors Demetris Lappas, Vasileios Argyriou, Dimitrios Makris, Kingston University, United Kingdom
SessionCI-4: Remote Sensing and Coded Aperture Imaging
LocationGather.Town
Session Time:Thursday, 10 June, 15:30 - 16:15
Presentation Time:Thursday, 10 June, 15:30 - 16:15
Presentation Poster
Topic Computational Imaging: [IMT] Computational Imaging Methods and Models
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
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 model. This paper introduces Fourier Transforms 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.