PANDA : Perceptually Aware Neural Detection of Anomalies

• URL: http://arxiv.org/abs/2104.13702v1
• Date: Wed, 28 Apr 2021 11:03:50 GMT
• Title: PANDA : Perceptually Aware Neural Detection of Anomalies
• Authors: Jack W. Barker and Toby P. Breckon
• Abstract summary: We propose a novel fine-grained VAE-GAN architecture trained in a semi-supervised manner 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.
• Score: 20.838700258121197
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 state of-the-art anomaly detection results when compared extensively with prior semi-supervised approaches across a multitude of anomaly detection benchmark tasks including trivial leave-one out 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).

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