Causal Future Prediction in a Minkowski Space-Time
Abstract
Estimating future events is a difficult task. Unlike humans, machine learning approaches are not regularized by a natural understanding of physics. In the wild, a plausible succession of events is governed by the rules of causality, which cannot easily be derived from a finite training set. In this paper we propose a novel theoretical framework to perform causal future prediction by embedding spatiotemporal information on a Minkowski space-time. We utilize the concept of a light cone from special relativity to restrict and traverse the latent space of an arbitrary model. We demonstrate successful applications in causal image synthesis and future video frame prediction on a dataset of images. Our framework is architecture- and task-independent and comes with strong theoretical guarantees of causal capabilities.
Cite
@article{arxiv.2008.09154,
title = {Causal Future Prediction in a Minkowski Space-Time},
author = {Athanasios Vlontzos and Henrique Bergallo Rocha and Daniel Rueckert and Bernhard Kainz},
journal= {arXiv preprint arXiv:2008.09154},
year = {2020}
}
Comments
Includes supplement