Date of Graduation


Document Type


Degree Type



Statler College of Engineering and Mineral Resources


Lane Department of Computer Science and Electrical Engineering

Committee Chair

Gianfranco Doretto

Committee Member

Donald Adjeroh

Committee Member

Hany Ammar

Committee Member

Katerina Goseva Popstojanova,

Committee Member

Edgar Fuller


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

the art.

Included in

Engineering Commons