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The recent rise in deep learning technologies fueled innovation and boostedscientific research. Their achievements enabled new research directions fordeep generative modeling (DGM), an increasingly popular approach that cancreate novel and unseen data, starting from a given data set. As the technologyshows promising applications, many ethical issues also arise. For example,their misuse can enable disinformation campaigns and powerful phishingattempts. Research also indicates different biases affect deep learning models,leading to social issues such as misrepresentation. In this work, we formulatea novel setting to deal with similar problems, showing that a repurposedanomaly detection system effectively generates novel data, avoiding generatingspecified unwanted data. We propose Variational Auto-encoding BinaryClassifiers (V-ABC): a novel model that repurposes and extends theAuto-encoding Binary Classifier (ABC) anomaly detector, using the VariationalAuto-encoder (VAE). We survey the limitations of existing approaches andexplore many tools to show the model's inner workings in an interpretable way.This proposal has excellent potential for generative applications: models thatrely on user-generated data could automatically filter out unwanted content,such as offensive language, obscene images, and misleading information.

Novel Applications for VAE-based Anomaly Detection Systems