Date of Graduation
Statler College of Engineering and Mineral Resources
Lane Department of Computer Science and Electrical Engineering
Katerina Goseva Popstojanova,
The problem of novelty or anomaly detection refers to the ability to automatically
identify data samples that differ from a notion of normality. Techniques
that address this problem are necessary in many applications, like in medical
diagnosis, autonomous driving, fraud detection, or cyber-attack detection, just to
mention a few. The problem is inherently challenging because of the openness of
the space of distributions that characterize novelty or outlier data points. This is
often matched with the inability to adequately represent such distributions due
to the lack of representative data.
In this dissertation we address the challenge above by making several contributions.
(a)We introduce an unsupervised framework for novelty detection,
which is based on deep learning techniques, and which does not require labeled
data representing the distribution of outliers. (b) The framework is general and
based on first principles by detecting anomalies via computing their probabilities
according to the distribution representing normality. (c) The framework can
handle high-dimensional data such as images, by performing a non-linear dimensionality
reduction of the input space into an isometric lower-dimensional space,
leading to a computationally efficient method. (d) The framework is guarded
from the potential inclusion of distributions of outliers into the distribution of
normality by favoring that only inlier data can be well represented by the model.
(e) The methods are evaluated extensively on multiple computer vision benchmark
datasets, where it is shown that they compare favorably with the state of
Almohsen, Ranya, "Learning Representations for Novelty and Anomaly Detection" (2023). Graduate Theses, Dissertations, and Problem Reports. 11639.