论文标题
深度学习模型解决了多种更改类型的更改点检测
Deep learning model solves change point detection for multiple change types
论文作者
论文摘要
变更点检测旨在引起数据分布中的突然障碍。常见方法假设数据只有两个固定分布：一个更改点后一个之前和另一个。现实世界数据比这个假设更丰富。更改之前和之后可以有多个不同的分布。我们提出了一种在多个分布场景中起作用的方法。我们的方法学习适用于更改点检测的半结构化数据的表示形式，而基于常见的分类器的方法失败了。此外，在预测变更点时，我们的模型更加健壮。用于基准测试的数据集是图像的序列，其中有和没有变化点。
A change points detection aims to catch an abrupt disorder in data distribution. Common approaches assume that there are only two fixed distributions for data: one before and another after a change point. Real-world data are richer than this assumption. There can be multiple different distributions before and after a change. We propose an approach that works in the multiple-distributions scenario. Our approach learn representations for semi-structured data suitable for change point detection, while a common classifiers-based approach fails. Moreover, our model is more robust, when predicting change points. The datasets used for benchmarking are sequences of images with and without change points in them.
