| Kurzfassung
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Hidden outliers represent instances of disagreement between a full-space and an ensemble. This adversarial nature naturally replicates the subspace behavior that high-dimensional outliers exhibit in reality. Due to this, they have been proven useful for representing complex occurrences like fraud, critical infrastructure failure, and healthcare data, as well as for their use in general outlier detection as the positive class of a self-supervised learner. However, while interesting, hidden outliers' quality highly depends on the number of subspaces selected in the ensemble out of the total possible. Since the number of subspaces increases exponentially with the number of features, this makes high-dimensional applications of Data Analysis, such as Computer Vision, computationally unfeasible. In this thesis, we are going to study the generation of hidden outliers on the embedded data manifold using deep learning techniques to overcome this issue. More precisely, we are going to study the behavior, characteristics, and performance in multiple use-cases of hidden outliers in the data manifold.
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