P-KDGAN: Progressive Knowledge Distillation with GANs for One-class Novelty Detection

Understand

One-class novelty detection is to identify anomalous instances that do not conform to the expected normal instances.

• In this paper, the Generative Adversarial Networks (GANs) based on encoder-decoder-encoder pipeline are used for detection and achieve state-of-the-art performance.
• However, deep neural networks are too over-parameterized to deploy on resource-limited devices.
• Therefore, Progressive Knowledge Distillation with GANs (PKDGAN) is proposed to learn compact and fast novelty detection networks.