Methods and systems for training a machine learning method using high quality ground truth approximations

Abstract

A computer-implemented method for training a machine-learning method comprises the following steps carried out by computer hardware components: determining measurement data from a first sensor; determining approximations of ground truths based on a second sensor; and training the machine-learning method based on the measurement data and the approximations of ground truths; wherein approximations of ground truths of lower-approximation quality have a lower effect on the training than approximations of ground truths of higher-approximation quality.

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