Anomaly detection is of paramount importance in many real-world domains, characterized by evolving behavior. Lifelong learning represents an emerging trend, answering the need for machine learning models that continuously adapt to new challenges in dynamic environments while retaining past knowledge. However, limited efforts are dedicated to building foundations for lifelong anomaly detection, which provides intrinsically different challenges compared to the more widely explored classification setting. In this paper, we face this issue by exploring, motivating, and discussing lifelong anomaly detection, trying to build foundations for its wider adoption. First, we explain why lifelong anomaly detection is relevant, defining challenges and opportunities to design anomaly detection methods that deal with lifelong learning complexities. Second, we characterize learning settings and a scenario generation procedure that enables researchers to experiment with lifelong anomaly detection using existing datasets. Third, we perform experiments with popular anomaly detection methods on proposed lifelong scenarios, emphasizing the gap in performance that could be gained with the adoption of lifelong learning. Overall, we conclude that the adoption of lifelong anomaly detection is important to design more robust models that provide a comprehensive view of the environment, as well as simultaneous adaptation and knowledge retention.
翻译:长期学习是一个新兴趋势,满足了对机器学习模型的需求,这些模型在动态环境中不断适应新的挑战,同时保留了过去的知识。然而,在为发现终身异常现象奠定基础方面所做的努力有限,这提供了与广泛探索的分类设置相比固有的不同挑战。在本文件中,我们通过探索、激励和讨论终生异常现象发现,试图为更广泛地采用这一发现奠定基础,来应对这一问题。首先,我们解释为什么终生异常现象发现具有相关性,界定了设计应对终身学习复杂性的异常现象发现方法的挑战和机会。第二,我们描述学习环境和情景生成程序,使研究人员能够利用现有数据集试验终生异常现象的检测。第三,我们用流行的异常现象检测方法对拟议的终身情景进行实验,强调采用终身学习可能取得的业绩差距。总体而言,我们的结论是,采用终身异常现象检测方法对于设计更强有力的模型非常重要,这些模型能够提供对环境的全面观点,同时进行适应和知识保留。</s>