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We investigate the effect of variational autoencoder (VAE) based anonymization on anomalous subgroup preservation. In particular, we train a binary classifier to discover the most anomalous subgroup in a dataset by maximizing the bias between the group?s
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Preservation Of Anomalous Subgroups On Variational Autoencoder Transformed Data
We investigate the effect of variational autoencoder (VAE) based anonymization on anomalous subgroup preservation. In particular, we train a binary classifier to discover the most anomalous subgroup in a dataset by maximizing the bias between the group?s