Y-GAN: Learning Dual Data Representations for Efficient Anomaly Detection
This work addresses anomaly detection for applications like image analysis, but it appears incremental as it builds on existing reconstruction-based methods with novel disentanglement techniques.
The authors tackled the problem of anomaly detection by proposing Y-GAN, a reconstruction-based model that learns dual latent representations to separate semantic and residual information, achieving efficient anomaly detection across multiple datasets like MNIST, FMNIST, CIFAR10, and PlantVillage.
We propose a novel reconstruction-based model for anomaly detection, called Y-GAN. The model consists of a Y-shaped auto-encoder and represents images in two separate latent spaces. The first captures meaningful image semantics, key for representing (normal) training data, whereas the second encodes low-level residual image characteristics. To ensure the dual representations encode mutually exclusive information, a disentanglement procedure is designed around a latent (proxy) classifier. Additionally, a novel consistency loss is proposed to prevent information leakage between the latent spaces. The model is trained in a one-class learning setting using normal training data only. Due to the separation of semantically-relevant and residual information, Y-GAN is able to derive informative data representations that allow for efficient anomaly detection across a diverse set of anomaly detection tasks. The model is evaluated in comprehensive experiments with several recent anomaly detection models using four popular datasets, i.e., MNIST, FMNIST and CIFAR10, and PlantVillage.