BALANCING DIVERSITY AND PRECISION OF GENERATIVE MODELS WITH COMPLEMENTARY DENSITY ESTIMATORS

摘要

Systems and methods for training and evaluating a deep generative model with an architecture consisting of two complementary density estimators are provided. The method includes receiving a probabilistic model of vehicle motion, and training, by a processing device, a first density estimator and a second density estimator jointly based on the probabilistic model of vehicle motion. The first density estimator determines a distribution of outcomes and the second density estimator estimates sample quality. The method also includes identifying by the second density estimator spurious modes in the probabilistic model of vehicle motion. The probabilistic model of vehicle motion is adjusted to eliminate the spurious modes.