论文标题
计算机视觉及其他方面的自我监督的异常检测：调查和前景
Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlook
论文作者
论文摘要
异常检测（AD）通过识别偏离正常行为的模式或事件，在包括网络安全，金融和医疗保健在内的各个领域中起着至关重要的作用。近年来，由于深度学习模型的显着增长，在该领域取得了重大进展。值得注意的是，自我监督的学习的出现引发了新颖的AD算法的发展，这些算法的表现优于现有的最新方法。本文旨在对自我监管的异常检测中当前的方法进行全面综述。我们介绍了标准方法的技术细节，并讨论了它们的优势和缺点。我们还将这些模型的性能相互比较彼此和其他最新的异常检测模型。最后，本文以讨论自我监督异常检测的未来方向的讨论，包括开发更有效，更有效的算法以及将这些技术与其他相关领域（例如多模式学习）的集成。
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.
