深度演化半监督异常检测
Deep evolving semi-supervised anomaly detection
AI总结：
论文形式化持续半监督异常检测任务，提出VAE基线与带离群点拒绝的深度生成回放方法，并验证EVT和EWC对比下的有效性。
摘要：
本文旨在形式化持续半监督异常检测（CSAD）任务，以强调这种尽可能贴近真实世界条件的问题表述的重要性。在概述持续半监督学习的相关定义、组成部分、异常检测扩展及训练协议之后，本文提出一种用于处理半监督数据的变分自编码器（VAE）基线模型，以及一种结合离群点拒绝的深度生成回放持续学习方法。结果表明，将极值理论（EVT）应用于异常检测，即使与联合训练这一上界基线相比，也能取得有前景的结果。结果探究了有标签与无标签数据的数量、所属类别及其在数据流中的位置所产生的影响。离群点拒绝展现出良好的初步结果，通常优于Elastic Weight Consolidation（EWC）基线方法。本文提出了CSAD基线，以及用于复现性和供其他从业者测试的具体数据集设置。未来研究方向包括其他CSAD设定，以及对高效持续超参数调优的进一步研究。
英文摘要：
The aim of this paper is to formalise the task of continual semi-supervised anomaly detection (CSAD), with the aim of highlighting the importance of such a problem formulation which assumes as close to real-world conditions as possible. After an overview of the relevant definitions of continual semi-supervised learning, its components, anomaly detection extension, and the training protocols; the paper introduces a baseline model of a variational autoencoder (VAE) to work with semi-supervised data along with a continual learning method of deep generative replay with outlier rejection. The results show that such a use of extreme value theory (EVT) applied to anomaly detection can provide promising results even in comparison to an upper baseline of joint training. The results explore the effects of how much labelled and unlabelled data is present, of which class, and where it is located in the data stream. Outlier rejection shows promising initial results where it often surpasses a baseline method of Elastic Weight Consolidation (EWC). A baseline for CSAD is put forward along with the specific dataset setups used for reproducability and testability for other practitioners. Future research directions include other CSAD settings and further research into efficient continual hyperparameter tuning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
