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PANDA: Perceptually Aware Neural Detection of Anomalies

Barker, J.W.; Breckon, T.P.

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Semi-supervised methods of anomaly detection have seen substantial advancement in recent years. Of particular interest are applications of such methods to diverse, real-world anomaly detection problems where anomalous variations can vary from the visually obvious to the very subtle. In this work, we propose a novel fine-grained VAE-GAN architecture trained in a semi-supervised manner in order to detect both visually distinct and subtle anomalies. With the use of a residually connected dual-feature extractor, a fine-grained discriminator and a perceptual loss function, we are able to detect subtle, low inter-class (anomaly vs. normal) variant anomalies with greater detection capability and smaller margins of deviation in AUC value during inference compared to prior work whilst also remaining time-efficient during inference. We achieve stateof-the-art anomaly detection results when compared extensively with prior semi-supervised approaches across a multitude of anomaly detection benchmark tasks including trivial leave-oneout tasks (CIFAR-10 - AUPRCavg: 0.91; MNIST - AUPRCavg: 0.90) in addition to challenging real-world anomaly detection tasks (plant leaf disease - AUC: 0.776; threat item X-ray - AUC: 0.51), video frame-level anomaly detection (UCSDPed1 - AUC: 0.95) and high frequency texture with object anomalous defect detection (MVTEC - AUCavg: 0.83).


Barker, J., & Breckon, T. (2021). PANDA: Perceptually Aware Neural Detection of Anomalies. .

Conference Name 2021 International Joint Conference on Neural Networks (IJCNN)
Conference Location Shenzhen, China
Start Date Jul 18, 2021
End Date Jul 22, 2021
Acceptance Date Apr 10, 2021
Online Publication Date Sep 23, 2021
Publication Date 2021
Deposit Date Apr 28, 2021
Publicly Available Date Apr 30, 2021
Publisher Institute of Electrical and Electronics Engineers


Accepted Conference Proceeding (941 Kb)

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