  Anomaly detection (AD) plays a crucial role in various domains, including
cybersecurity, finance, and healthcare, by identifying patterns or events that
deviate from normal behaviour. In recent years, significant progress has been
made in this field due to the remarkable growth of deep learning models.
Notably, the advent of self-supervised learning has sparked the development of
novel AD algorithms that outperform the existing state-of-the-art approaches by
a considerable margin. This paper aims to provide a comprehensive review of the
current methodologies in self-supervised anomaly detection. We present
technical details of the standard methods and discuss their strengths and
drawbacks. We also compare the performance of these models against each other
and other state-of-the-art anomaly detection models. Finally, the paper
concludes with a discussion of future directions for self-supervised anomaly
detection, including the development of more effective and efficient algorithms
and the integration of these techniques with other related fields, such as
multi-modal learning.
