Causal Future Prediction in a Minkowski Space-Time
It addresses the challenge of enabling machine learning models to understand causality for future event prediction, which is incremental by applying physics-inspired constraints to existing methods.
The paper tackles the problem of causal future prediction by embedding spatiotemporal information in a Minkowski space-time, using light cones from special relativity to restrict latent spaces, and demonstrates successful applications in causal image synthesis and future video frame prediction on image datasets.
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.