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Anomaly Detection Based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation

Ramirez Rivera, Adin; Khan, Adil; Bekkouch, Imad Eddine Ibrahim; Sheikh, Taimoor Shakeel

Authors

Adin Ramirez Rivera

Imad Eddine Ibrahim Bekkouch

Taimoor Shakeel Sheikh



Abstract

Anomaly detection suffers from unbalanced data since anomalies are quite rare. Synthetically generated anomalies are a solution to such ill or not fully defined data. However, synthesis requires an expressive representation to guarantee the quality of the generated data. In this article, we propose a two-level hierarchical latent space representation that distills inliers' feature descriptors [through autoencoders (AEs)] into more robust representations based on a variational family of distributions (through a variational AE) for zero-shot anomaly generation. From the learned latent distributions, we select those that lie on the outskirts of the training data as synthetic-outlier generators. Also, we synthesize from them, i.e., generate negative samples without seen them before, to train binary classifiers. We found that the use of the proposed hierarchical structure for feature distillation and fusion creates robust and general representations that allow us to synthesize pseudo outlier samples. Also, in turn, train robust binary classifiers for true outlier detection (without the need for actual outliers during training). We demonstrate the performance of our proposal on several benchmarks for anomaly detection.

Citation

Ramirez Rivera, A., Khan, A., Bekkouch, I. E. I., & Sheikh, T. S. (2022). Anomaly Detection Based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation. IEEE Transactions on Neural Networks and Learning Systems, 33(1), 281-291. https://doi.org/10.1109/TNNLS.2020.3027667

Journal Article Type Article
Acceptance Date Sep 24, 2020
Online Publication Date Oct 16, 2020
Publication Date Jan 1, 2022
Deposit Date Aug 28, 2024
Publicly Available Date Sep 13, 2024
Journal IEEE Transactions on Neural Networks and Learning Systems
Print ISSN 2162-237X
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Peer Reviewed Peer Reviewed
Volume 33
Issue 1
Pages 281-291
DOI https://doi.org/10.1109/TNNLS.2020.3027667
Keywords Feature extraction; Unsupervised learning
Public URL https://hull-repository.worktribe.com/output/4792237

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