Deep learning model solves change point detection for multiple change types

• URL: http://arxiv.org/abs/2204.07403v1
• Date: Fri, 15 Apr 2022 09:44:21 GMT
• Title: Deep learning model solves change point detection for multiple change types
• Authors: Alexander Stepikin, Evgenia Romanenkova, Alexey Zaytsev
• Abstract summary: A change points detection aims to catch an abrupt disorder in data distribution. We propose an approach that works in the multiple-distributions scenario.
• Score: 69.77452691994712
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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