Method for training an image classifier that processes an input image in a sequence of multiple convolutional layers to form an intermediate product and maps this intermediate product, with at least one classifier layer, to one or more classification scores with regard to one or more predefined classes, comprising the following steps: • providing training images, wherein at least a first subset of the training images belongs to a first distribution, at least a second subset of the training images belongs to a second distribution and these two distributions (5a, 5b) correspond to different domains of training images; • providing target classification scores to which the image classifier is intended to nominally map training images in the fully trained state; • supplying the training images to the image classifier as input images, and the image classifier mapping each of said images to one or more classification scores; • supplying at least one resulting output (11a-13a) of a convolutional layer to a domain classifier, and this domain classifier mapping said at least one output to domain assignments of the training image for different domains, wherein o a domain classifier having at least one fully connected layer is selected, and wherein spatial selections from the output (11a-13a) of the convolutional layer are fed into this fully connected layer or o a domain classifier that generates a segmentation of the output (11a-13a) of the convolutional layer by domain is selected; • parameters characterizing the behaviour of the image classifier are optimized with the objective, in the further processing of training images, of minimizing a deviation of the classification scores delivered by the image classifier from the target classification scores, while maximizing a deviation of the domain assignments from the actual domains of the respective training images, wherein training images are selected from domains that correspond to different configurations of the sensor systems used for image capture, wherein at least one sensor system delivers camera images, characterized in that at least one further sensor system delivers radar images. • providing training images, wherein at least a first subset of the training images belongs to a first distribution, at least a second subset of the training images belongs to a second distribution and these two distributions (5a, 5b) correspond to different domains of training images; • providing target classification scores to which the image classifier is intended to nominally map training images in the fully trained state; • supplying the training images to the image classifier as input images, and the image classifier mapping each of said images to one or more classification scores; • supplying at least one resulting output (11a-13a) of a convolutional layer to a domain classifier, and this domain classifier mapping said at least one output to domain assignments of the training image for different domains, wherein o a domain classifier having at least one fully connected layer is selected, and wherein spatial selections from the output (11a-13a) of the convolutional layer are fed into this fully connected layer or o a domain classifier that generates a segmentation of the output (11a-13a) of the convolutional layer by domain is selected; o a domain classifier having at least one fully connected layer is selected, and wherein spatial selections from the output (11a-13a) of the convolutional layer are fed into this fully connected layer or o a domain classifier that generates a segmentation of the output (11a-13a) of the convolutional layer by domain is selected; • parameters characterizing the behaviour of the image classifier are optimized with the objective, in the further processing of training images, of minimizing a deviation of the classification scores delivered by the image classifier from the target classification scores, while maximizing a deviation of the domain assignments from the actual domains of the respective training images,