About: We propose a new method for novelty detection that can tolerate nontrivial corruption of the training points. 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 corruption, we incorporate three changes to the common VAE: 1. Modeling the latent distribution as a mixture of Gaussian inliers and outliers, while using only the inlier component when testing; 2. Applying the Wasserstein-1 metric for regularization, instead of Kullback-Leibler divergence; and 3. Using a least absolute deviation error for reconstruction, which is equivalent to assuming a heavy-tailed likelihood. We illustrate state-of-the-art results on standard benchmark datasets for novelty detection.   Goto Sponge  NotDistinct  Permalink

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  • We propose a new method for novelty detection that can tolerate nontrivial corruption of the training points. 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 corruption, we incorporate three changes to the common VAE: 1. Modeling the latent distribution as a mixture of Gaussian inliers and outliers, while using only the inlier component when testing; 2. Applying the Wasserstein-1 metric for regularization, instead of Kullback-Leibler divergence; and 3. Using a least absolute deviation error for reconstruction, which is equivalent to assuming a heavy-tailed likelihood. We illustrate state-of-the-art results on standard benchmark datasets for novelty detection.
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  • Data mining
  • Machine learning
  • Thermodynamics
  • Neurophysiology
  • Patent law
  • Statistical outliers
  • F-divergences
  • Experimental psychology
  • Petrology
  • Rock formations
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