PANDA : Perceptually Aware Neural Detection of Anomalies

TL;DR

PANDA introduces a semi-supervised, perceptually aware neural architecture that significantly improves detection of both obvious and subtle anomalies across diverse real-world and benchmark datasets.

Contribution

The paper presents a novel fine-grained VAE-GAN architecture with dual-feature extraction and perceptual loss for enhanced anomaly detection capabilities.

Findings

Achieves state-of-the-art results across multiple benchmark datasets.

Detects subtle anomalies with smaller deviation margins in AUC.

Remains time-efficient during inference.

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).