  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.
