Causal Future Prediction in a Minkowski Space-Time

Athanasios Vlontzos; Henrique Bergallo Rocha; Daniel Rueckert; Bernhard Kainz

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arXiv · 2008.09154

Causal Future Prediction in a Minkowski Space-Time

Athanasios Vlontzos·Henrique Bergallo Rocha·Daniel Rueckert·Bernhard Kainz

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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.

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Athanasios Vlontzos, Henrique Bergallo Rocha, Daniel Rueckert, Bernhard Kainz. 2020-08-20. Causal Future Prediction in a Minkowski Space-Time. https://arxiv.org/abs/2008.09154

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