VAE-based Deep SVDD for anomaly detection

Anomaly detection is an essential task for different fields in the real world. The imbalanced data and lack of labels make the task challenging. Deep learning models based on autoencoder (AE) have been applied to address the above difficulties successfully. However, in these AE-based deep methods, the AE-based model’s optimization and the anomaly detector design are separated. Therefore, the latent representations in AE are less relevant for the anomaly detection task, which reduces the accuracy of anomaly detection. A deep support vector data description based on variational autoencoder (Deep SVDD-VAE) is proposed in this paper to solve this problem. In the proposed model, VAE is used to reconstruct the input instances, while a spherical discriminative boundary is learned with the latent representations simultaneously based on SVDD. Unlike existing AE-based methods, we seek the model parameters via the joint optimization of VAE and SVDD, which ensures the separability of the latent representations. Experimental results on MNIST, CIFAR-10, and GTSRB datasets show the effectiveness of Deep SVDD-VAE.