In a method and a computer for determining a training function in order to generate annotated training images, a training image and training-image information are provided to a computer, together with an isolated item of image information that is independent of the training image. A first calculation is made in the computer by applying an image-information-processing first function to the isolated item of image information, and a second calculation is made by applying an image-information-processing second function to the training image. Adjustments to the first and second functions are made based on these calculation results, from which a determination of a training function is then made in the computer.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for determining a training function comprising: in a computer, receiving a first reception of a training image and an item of training-image information via an interface of the computer, wherein the training-image information is image information for the training image; in said computer, receiving a second reception of an isolated item of image information via the interface, wherein the isolated item of image information is independent of the training image; in said computer, making a first calculation of a first result of an image-information-processing first function when applied to the isolated item of image information; in said computer, making a second calculation of a second result of an image-processing second function when applied to the training image; in said computer, making an adjustment of a parameter of at least one of the image-information-processing first function or the image-processing second function, based on at least the first result and the second result; and in said computer, making a determination of a training function based on the image-information-processing first function wherein, when applied to an item of image information, the training function generates an associated image as an output of the computer. 2. The method as claimed in claim 1, wherein at least one of the first function or the second function is provided by an artificial neural network, and wherein the parameters of the artificial neural network comprise edge weights of the artificial neural network. 3. The method as claimed in claim 2, wherein the adjustment comprises adjusting the edge weights by minimizing a cost function by execution of backpropagation. 4. The method as claimed in claim 1, wherein said item of image information of an image comprises segmentation of the image into at least one image region. 5. The method as claimed in claim 1, wherein said item of image information of an image comprises a variable that assesses whether a defined object or a plurality of defined objects are depicted in the image. 6. The method as claimed in claim 1, wherein the image-information-processing first function is a generator function that, when applied to said item of image information, generates an associated image as said output; wherein the image-processing second function is a classification function that, when applied to said image, generates an associated item of image information as said output; wherein the first result is a calculated image; wherein the second result is a first item of calculated image information; wherein the training function is the image-information-processing first function; and wherein the method furthermore comprises making a third calculation in the computer of a second item of calculated image information by applying the image-processing function to the calculated image. 7. The method as claimed in claim 6, wherein the first item of calculated image information comprises an estimation of a first probability of the training image being contained in a set of training images; and wherein the second item of calculated image information comprises an estimation of a second probability of the calculated image being contained in a set of training images. 8. The method as claimed in claim 7, wherein at least one of the first function or the second function is provided by an artificial neural network, and wherein the parameters of the artificial neural network comprise edge weights of the artificial neural network; wherein the adjustment comprises adjusting the edge weights by minimizing a cost function by execution of backpropagation; and wherein the cost function is based on at least a first difference of the first item of calculated image information from the training-image information. 9. The method as claimed in claim 8, wherein the cost function is furthermore based on at least a second difference of the second item of calculated image information from the isolated item of image information. 10. The method as claimed in claim 1, wherein at least one of the first function or the second function is provided by an artificial neural network, and wherein the parameters of the artificial neural network comprise edge weights of the artificial neural network; wherein the image-information-processing first function is an information autoencoder that, when applied to said first item of image information, generates a second item of image information as said output; wherein the image-processing second function is an image autoencoder that, when applied to a first image, generates a second image as said output; wherein a central layer of the information autoencoder and a central layer of the image autoencoder have a same number of central nodes; wherein the first result corresponds to first node values; wherein the first node values are values of the nodes of the central layer of the information autoencoder when the isolated item of image information is the input value of the information autoencoder; wherein the second result corresponds to second node values; wherein the second node values are values of the nodes of the central layer of the image autoencoder when the training image is the input value of the image autoencoder; wherein the method furthermore comprises making a further calculation of further node values that are values of the nodes of the central layer of the information autoencoder when the training-image information is the input value of the information autoencoder; and wherein the training function is composed of the first part of the information autoencoder with the second part of the image autoencoder. 11. The method as claimed in claim 10, wherein at least one of the first function or the second function is provided by an artificial neural network, and wherein the parameters of the artificial neural network comprise edge weights of the artificial neural network; wherein the adjustment comprises adjusting the edge weights by minimizing a cost function by execution of backpropagation; and wherein a distance between the first node values and the second node values makes a negative contribution to the cost function and wherein a distance between the second node values and the third node values makes a positive contribution to the cost function. 12. The method as claimed in claim 10, wherein: the training function generates an image as an output value from an item of image information as an input value such that an item of image information is used as an input value of the information autoencoder; the node values of the central layer of the information autoencoder are transferred to the node values of the central layer of the image autoencoder; and the output value of the training function corresponds to the resulting output value of the image autoencoder. 13. A function-determining computer comprising: a processor; an interface that receives a first reception of a training image and an item of training-image information into said processor, wherein the training-image information is image information for the training image; said interface also receiving a second reception of an isolated item of image information into said processor, wherein the isolated item of image information is independent of the training image; said processor being configured to make a first calculation of a first result of an image-information-processing first function when applied to the isolated item of image information; said processor being configured to make a second calculation of a second result of an image-processing second function when applied to the training image; said processor being configured to make an adjustment of a parameter of at least one of the image-information-processing first function or the image-processing second function, based on at least the first result and the second result; and said processor being configured to make a determination of a training function based on the image-information-processing first function wherein, when applied to an item of image information, the training function generates an associated image as an output of the computer. 14. A non-transitory, computer-readable data storage medium encoded with programming instructions, said storage medium being loaded into a computer and said programming instructions causing said computer to: receive a first reception of a training image and an item of training-image information via an interface of the computer, wherein the training-image information is image information for the training image; receive a second reception of an isolated item of image information via the interface, wherein the isolated item of image information is independent of the training image; make a first calculation of a first result of an image-information-processing first function when applied to the isolated item of image information; make a second calculation of a second result of an image-processing second function when applied to the training image; make an adjustment of a parameter of at least one of the image-information-processing first function or the image-processing second function, based on at least the first result and the second result; and make a determination of a training function based on the image-information-processing first function wherein, when applied to an item of image information, the training function generates an associated image as an output of the computer.