Deep Anomaly Detection in Text
Andrei Manolache
Deep anomaly detection methods have become increasingly popular in recent
years, with methods like Stacked Autoencoders, Variational Autoencoders, and
Generative Adversarial Networks greatly improving the state-of-the-art. Other
methods rely on augmenting classical models (such as the One-Class Support
Vector Machine), by learning an appropriate kernel function using Neural
Networks. Recent developments in representation learning by self-supervision
are proving to be very beneficial in the context of anomaly detection. Inspired
by the advancements in anomaly detection using self-supervised learning in the
field of computer vision, this thesis aims to develop a method for detecting
anomalies by exploiting pretext tasks tailored for text corpora. This approach
greatly improves the state-of-the-art on two datasets, 20Newsgroups, and AG
News, for both semi-supervised and unsupervised anomaly detection, thus proving
the potential for self-supervised anomaly detectors in the field of natural
language processing.