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A change points detection aims to catch an abrupt disorder in datadistribution. Common approaches assume that there are only two fixeddistributions for data: one before and another after a change point. Real-worlddata are richer than this assumption. There can be multiple differentdistributions before and after a change. We propose an approach that works inthe multiple-distributions scenario. Our approach learn representations forsemi-structured data suitable for change point detection, while a commonclassifiers-based approach fails. Moreover, our model is more robust, whenpredicting change points. The datasets used for benchmarking are sequences ofimages with and without change points in them.

Deep learning model solves change point detection for multiple change types