论文标题
基于分类的一般数据的异常检测
Classification-Based Anomaly Detection for General Data
论文作者
论文摘要
异常检测发现，发现与以前所见的模式相偏离，这是人工智能的基本问题之一。最近，基于分类的方法被证明可以在此任务上取得卓越的结果。在这项工作中，我们提出了一个统一的观点，并提出了一种开放式方法，goad，以放大当前的概括假设。此外，我们使用随机仿射变换扩展了基于转换方法的适用性。我们的方法显示出可获得最先进的准确性，并且适用于广泛的数据类型。我们方法的强大性能在来自不同域的多个数据集上得到了广泛的验证。
Anomaly detection, finding patterns that substantially deviate from those seen previously, is one of the fundamental problems of artificial intelligence. Recently, classification-based methods were shown to achieve superior results on this task. In this work, we present a unifying view and propose an open-set method, GOAD, to relax current generalization assumptions. Furthermore, we extend the applicability of transformation-based methods to non-image data using random affine transformations. Our method is shown to obtain state-of-the-art accuracy and is applicable to broad data types. The strong performance of our method is extensively validated on multiple datasets from different domains.
