  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).
