Novelty Detection via Robust Variational Autoencoding

• URL: http://arxiv.org/abs/2006.05534v3
• Date: Wed, 7 Oct 2020 00:56:34 GMT
• Title: Novelty Detection via Robust Variational Autoencoding
• Authors: Chieh-Hsin Lai, Dongmian Zou and Gilad Lerman
• Abstract summary: We propose a new method for novelty detection that can tolerate high corruption of the training points. Our method trains a robust variational autoencoder (VAE), which aims to generate a model for the uncorrupted training points.
• Score: 13.664682865991255
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a new method for novelty detection that can tolerate high corruption of the training points, whereas previous works assumed either no or very low corruption. Our method trains a robust variational autoencoder (VAE), which aims to generate a model for the uncorrupted training points. To gain robustness to high corruption, we incorporate the following four changes to the common VAE: 1. Extracting crucial features of the latent code by a carefully designed dimension reduction component for distributions; 2. Modeling the latent distribution as a mixture of Gaussian low-rank inliers and full-rank outliers, where the testing only uses the inlier model; 3. Applying the Wasserstein-1 metric for regularization, instead of the Kullback-Leibler (KL) divergence; and 4. Using a least absolute deviation error for reconstruction. We establish both robustness to outliers and suitability to low-rank modeling of the Wasserstein metric as opposed to the KL divergence. We illustrate state-of-the-art results on standard benchmarks for novelty detection.

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