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. It uses a two-component mix-ture model for each feature, with one component for clean data, and the other component that robusti es the model by isolating outliers.
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  • As in VAE-ROC, we use the rank-one approximation for the covariance in the Gaussian copula, to capture the local dependency structure in the mixed data.
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    A trivial autoencoder (TAE) is used to change data representation, and modified generative adversarial networks (GAN) are used to create new instances from random noise.

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