Abstract: A computer-implemented method of estimating a reliability of control data for a computer-controlled system interacting with an environment. The control data is inferred from a model input by a machine learnable control model which is trained on a training dataset. The model input comprises at least one direction vector which is extracted from sensor data and which is associated with a component of the computer-controlled system or an object in the environment. The reliability is estimated using a generative model that is trained to generate synthetic model inputs representative of the training dataset, by applying an inverse of the generative model to the model input to determine a likelihood of the model input being generated according to the generative model. The generative model comprises a coupling layer comprising a circle transformation and one or more of an unconditional rotation and a conditional rotation.

Abstract: A computer-implemented method of estimating a reliability of control data for a computer-controlled system interacting with an environment. The control data is inferred from a model input by a machine learnable control model which is trained on a training dataset. The model input comprises at least one direction vector which is extracted from sensor data and which is associated with a component of the computer-controlled system or an object in the environment. The reliability is estimated using a generative model that is trained to generate synthetic model inputs representative of the training dataset, by applying an inverse of the generative model to the model input to determine a likelihood of the model input being generated according to the generative model. The generative model comprises a coupling layer comprising a circle transformation and one or more of an unconditional rotation and a conditional rotation.