Distribution Preserving Multiple Hypotheses Prediction for Uncertainty Modeling

• URL: http://arxiv.org/abs/2110.02858v1
• Date: Wed, 6 Oct 2021 15:36:17 GMT
• Title: Distribution Preserving Multiple Hypotheses Prediction for Uncertainty Modeling
• Authors: Tobias Leemann, Moritz Sackmann, J\"orn Thielecke, Ulrich Hofmann
• Abstract summary: We propose an alternative loss for preserving the Multiple Hypotheses Prediction approach. We empirically show that our approach yields more representative hypotheses on a synthetic and a real-world motion prediction data set. The outputs of the proposed method can directly be used in sampling-based Monte-Carlo methods.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many supervised machine learning tasks, such as future state prediction in dynamical systems, require precise modeling of a forecast's uncertainty. The Multiple Hypotheses Prediction (MHP) approach addresses this problem by providing several hypotheses that represent possible outcomes. Unfortunately, with the common $l_2$ loss function, these hypotheses do not preserve the data distribution's characteristics. We propose an alternative loss for distribution preserving MHP and review relevant theorems supporting our claims. Furthermore, we empirically show that our approach yields more representative hypotheses on a synthetic and a real-world motion prediction data set. The outputs of the proposed method can directly be used in sampling-based Monte-Carlo methods.

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