论文标题
通过深度度量学习和端到端优化的深度度量学习无监督的异常检测
Unsupervised Anomaly Detection via Deep Metric Learning with End-to-End Optimization
论文作者
论文摘要
我们研究了无监督的高维数据的异常检测，并引入了基于深度度量学习（DML）的框架。特别是，我们通过深层神经网络学习了一个距离指标。通过此度量，我们将数据投影到度量空间中，以更好地将异常与正常数据分开，并降低维数诅咒对高维数据的影响。我们通过自学意识介绍了一种新颖的数据蒸馏方法，以纠正将所有数据正常假设的常规实践。我们还采用了DML文献中的硬采矿技术。我们显示这些组件提高了模型的性能，并大大减少了运行时间。通过在14个现实世界数据集上进行的一系列实验，我们的方法表现出与最先进的无监督异常检测方法相比的显着性能增长，例如，在14个数据集中平均值的绝对改善在4.44％和11.74％之间。此外，我们在Github上共享方法的源代码，以促进进一步的研究。
We investigate unsupervised anomaly detection for high-dimensional data and introduce a deep metric learning (DML) based framework. In particular, we learn a distance metric through a deep neural network. Through this metric, we project the data into the metric space that better separates the anomalies from the normal data and reduces the effect of the curse of dimensionality for high-dimensional data. We present a novel data distillation method through self-supervision to remedy the conventional practice of assuming all data as normal. We also employ the hard mining technique from the DML literature. We show these components improve the performance of our model and significantly reduce the running time. Through an extensive set of experiments on the 14 real-world datasets, our method demonstrates significant performance gains compared to the state-of-the-art unsupervised anomaly detection methods, e.g., an absolute improvement between 4.44% and 11.74% on the average over the 14 datasets. Furthermore, we share the source code of our method on Github to facilitate further research.
