Practical applications of artificial intelligence increasingly often have to deal with the streaming properties of real data, which, considering the time factor, are subject to phenomena such as periodicity and more or less chaotic degeneration - resulting directly in the concept drifts. The modern concept drift detectors almost always assume immediate access to labels, which due to their cost, limited availability and possible delay has been shown to be unrealistic. This work proposes an unsupervised Parallel Activations Drift Detector, utilizing the outputs of an untrained neural network, presenting its key design elements, intuitions about processing properties, and a pool of computer experiments demonstrating its competitiveness with state-of-the-art methods.

翻译：人工智能的实际应用日益需要处理真实数据的流式特性，这些数据在时间因素影响下会呈现周期性及不同程度的混沌退化现象，这些现象直接导致概念漂移的产生。现代概念漂移检测方法几乎都默认能即时获取数据标签，然而由于标签获取成本高、可用性有限及可能存在延迟，这种假设已被证明是不切实际的。本研究提出一种无监督的并行激活漂移检测器，该方法利用未经训练的神经网络输出，阐述了其核心设计要素、对处理特性的理论阐释，并通过大量计算机实验证明其与前沿方法相比具有竞争优势。