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A Semi-supervised Generalized VAE Framework for Abnormality Detection using One-Class Classification
Journal
Proceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022
Date Issued
2022-01-01
Author(s)
Sharma, Renuka
Mashkaria, Satvik
Awate, Suyash P.
Abstract
Abnormality 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 train on data solely from the inlier class, but some recent OCC methods have proposed semi-supervised extensions that also leverage a small amount of training data from outlier classes. Other recent methods extend existing principles to employ deep neural network (DNN) models for learning (for the inlier class) either latent-space distributions or autoencoders, but not both. We propose a semi-supervised variational formulation, leveraging generalized-Gaussian (GG) models leading to data-adaptive, robust, and uncertainty-aware distribution modeling in both latent space and image space. We propose a reparameterization for sampling from the latent-space GG to enable backpropagation-based optimization. Results on many publicly available real-world image sets and a synthetic image set show the benefits of our method over existing methods.
Subjects