# Deep learning model solves change point detection for multiple change types

arXiv: https://arxiv.org/abs/2204.07403 · HTML: https://mlsanity.com/paper/2204.07403/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 15 Apr 2022.

## Builds on (most cited first, 3 of 3)

- [An Evaluation of Change Point Detection Algorithms](https://mlsanity.com/paper/2003.06222/) (2020)
- [Real-time data-driven detection of the rock type alteration during a directional drilling](https://mlsanity.com/paper/1903.11436/) (2019)
- [Detecting Performance Degradation of Software-Intensive Systems in the Presence of Trends and Long-Range Dependence](https://mlsanity.com/paper/1609.07662/) (2016)

## Abstract (arXiv)

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.