Deep learning model solves change point detection for multiple change types
This addresses a limitation in real-world data analysis where existing methods assume only two fixed distributions, making it incremental by extending to multiple distributions.
The paper tackles the problem of change point detection in data with multiple distribution types, proposing a deep learning model that learns representations for semi-structured data and achieves robustness in predicting change points.
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.