Data transformations (e.g. rotations, reflections, and cropping) play an important role in self-supervised learning. Typically, images are transformed into different views, and neural networks trained on tasks involving these views produce useful feature representations for downstream tasks, including anomaly detection. However, for anomaly detection beyond image data, it is often unclear which transformations to use. Here we present a simple end-to-end procedure for anomaly detection with learnable transformations. The key idea is to embed the transformed data into a semantic space such that the transformed data still resemble their untransformed form, while different transformations are easily distinguishable. Extensive experiments on time series demonstrate that our proposed method outperforms existing approaches in the one-vs.-rest setting and is competitive in the more challenging n-vs.-rest anomaly detection task. On tabular datasets from the medical and cyber-security domains, our method learns domain-specific transformations and detects anomalies more accurately than previous work.
翻译:数据转换(例如旋转、反射和裁剪)在自我监督的学习中起着重要作用。 通常, 图像被转化成不同的观点, 神经网络在涉及这些观点的任务方面受过培训, 产生下游任务包括异常点探测的有用特征。 但是, 在图像数据之外, 要检测异常点, 通常还不清楚要使用哪些变异点。 我们在这里展示了一个简单的端到端程序, 以便通过可学习的变换来检测异常点。 关键的想法是将变换的数据嵌入一个语义空间, 使变换的数据仍然类似其非变换形式, 而不同的变换则容易辨别。 时间序列的广泛实验显示, 我们所拟议的方法比一- v.- rest 设置中的现有方法要优于更具有挑战性的 n- v.- rest 异常点探测任务。 在来自医疗和网络安全域的表格数据集中, 我们的方法比以前的工作更精确地学习了特定区域变换和检测异常点。