A Semi-Supervised Generalized VAE Framework for Abnormality Detection Using One-Class Classification

Renuka Sharma, Satvik Mashkaria, Suyash P. Awate; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022, pp. 595-603

Anomaly detection is a one-class classification (OCC) problem where the methods learn either a generative model of the inlier class (e.g., in the variants of kernel principal component analysis) or a decision boundary to encapsulate the inlier class (e.g., in the one-class variants of the support vector machine). Learning schemes for OCC typically rely on training data solely from the inlier class, but some recent approaches have proposed semi-supervised extensions, e.g., variants of semi-supervised anomaly detection that also leverage a small amount of training data from outlier classes. Other recent methods extend existing principles to employ deep neural network (DNN) modeling that relies on learning (for the inlier class) either latent-space distributions or autoencoders, but not both. We propose a novel semi-supervised variational formulation, leveraging generalized-Gaussian models leading to data-adaptive, robust, and uncertainty-aware distribution modeling in both latent space and image space. For variational learning, we propose a novel reparameterization for sampling from the latent-space generalized-Gaussian to enable backpropagation-based optimization. Results on several public image sets show the benefits of our method over state of the art.