Abstract
This paper examines the effectiveness of combining active learning and transfer learning for anomaly detection in cross-domain time-series data. Our results indicate that there is an interaction between clustering and active learning and in general the best performance is achieved using a single cluster (in other words when clustering is not applied). Also, we find that adding new samples to the training set using active learning does improve model performance but that in general, the rate of improvement is slower than the results reported in the literature suggest. We attribute this difference to an improved experimental design where distinct data samples are used for the sampling and testing pools. Finally, we assess the ceiling performance of transfer learning in combination with active learning across several datasets and find that performance does initially improve but eventually begins to tail off as more target points are selected for inclusion in training. This tail-off in performance may indicate that the active learning process is doing a good job of sequencing data points for selection, pushing the less useful points towards the end of the selection process and that this tail-off occurs when these less useful points are eventually added. Taken together our results indicate that active learning is effective but that the improvement in model performance follows a linear flat function concerning the number of points selected and labelled.
Abstract (translated)
本文研究了在跨域时间序列数据异常检测中结合主动学习和迁移学习的有效性。我们的结果显示，聚类与主动学习之间存在相互作用，并且总体而言，在不应用聚类的情况下（即使用单个簇时）可以获得最佳性能。此外，我们发现通过主动学习向训练集添加新样本确实可以提高模型的性能，但通常改善的速度比文献报道的结果要慢。我们认为这种差异是由于改进了实验设计，其中用于采样和测试的数据样本是不同的。 最后，我们在多个数据集中评估了迁移学习与主动学习相结合时的天花板性能，并发现其初始性能有所提升，但在选择更多目标点进行训练后，性能最终开始下降。这种性能下降可能表明，主动学习过程很好地为选定点进行了排序，将不太有用的点推到了选择过程的末尾，而当这些不太有用的点被加入到训练集中时，便出现了这一现象。 综合来看，我们的结果表明，主动学习是有效的，但模型性能的提升与所选取和标记的数据点数量之间的关系遵循一种线性且渐进的函数。
URL
https://arxiv.org/abs/2508.03921
