Patent Publication Number: US-2022222929-A1

Title: Method and device for testing the robustness of an artificial neural network

Description:
FIELD 
     The present invention relates to a method for testing the robustness of an artificial neural network, a method for training the artificial neural network, a method for operating this artificial neural network, a training device, a computer program and a machine-readable memory medium 
     BACKGROUND INFORMATION 
     “CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training”, arXiv preprint arXiv: 1703.10155, 2017, Jianmin Bao, Dong Chen, Fang Wen, Houqiang Li, and Gang Hua provides an overview of conventional generative methods such as variational autoencoders and generative adversarial networks. 
     SUMMARY 
     An example embodiment of the present invention may have the advantage that a novel generative model is made available that is advantageously suitable both for the augmentation of image data and for an anomaly detection. 
     Additional aspects of the present invention are disclosed herein. Advantageous further developments and embodiments of the present invention are disclosed herein. 
     In a first aspect, the present invention relates to a computer-implemented neural network system. In accordance with an example embodiment of the present invention, the system includes a first machine learning system, which is also denoted as a generator, in particular a first neural network; a second machine learning system, also denoted as an encoder, in particular a second neural network; and a third machine learning system, also denoted as a discriminator, in particular a third neural network; the first machine learning system being developed to ascertain a higher-dimensional, constructed image from a predefinable low-dimensional latent variable; the second machine learning system being developed to ascertain the latent variable again from the higher-dimensional constructed image; and the third machine learning system being developed to distinguish whether or not an image it receives is a real image, i.e., an image recorded by a sensor. This reversed autoencoder of this network system offers the advantage that the dependency of latent features (such as the hair color of detected pedestrians) is extractable in a particularly simple manner so that augmentations of training datasets are especially easy. At the same time, an anomaly detection is able to be carried out in a particularly robust manner because the system is trainable in an adversarial fashion. 
     In another independent aspect, the present invention relates to a method for training the neural network system. In accordance with an example embodiment of the present invention, the first machine learning system, and especially only the first machine learning system, being trained to the effect that an activation in a predefinable feature map of the feature maps of the third machine learning system ideally assumes the same value when it receives a real image or an image of the real image reconstructed from a series connection made up of the second machine learning system and the first machine learning system. It has been shown that the training converges particularly well in this way. 
     In a further development of this aspect according to the present invention, it may be provided that the first machine learning system is trained also to the effect that the third machine learning system ideally does not recognize that an image it receives that was generated by the first machine learning system is no real image. This ensures a particularly robust anomaly detection. 
     Alternatively or additionally, it may be provided that the second machine learning system, and especially only the second machine learning system, is trained to the effect that a reconstruction of the latent variable ascertained by a series connection made up of the first machine learning system and the second machine learning system ideally is similar to the latent variable. It was recognized that the convergence of the method is considerably improved if this reconstruction is selected in such a way that only the parameters of the second machine learning system are trained because the cost functions of the encoder and the generator are otherwise difficult to be brought in line with each other. 
     In order to achieve the best possible improvement in the training result, in a further refinement of the present invention, it may be provided that the third machine learning system is trained to the effect that it ideally recognizes that an image it has received that was generated by the first machine learning system is no real image and/or that the third machine learning system is trained also to the effect that it ideally recognizes that an image it has received is a real image. 
     In a further independent aspect, the present invention relates to a method for monitoring the correct method of functioning of a machine learning system, in particular a fourth neural network, for the classification and/or the semantic segmentation of an input image (x) it receives, such as for detecting pedestrians and/or other road users and/or road signs, and a monitoring unit, which includes the first machine learning system trained by one of the above-described methods and the second machine learning system of the neural network system, the input image being conveyed to the second machine learning system, which ascertains a low-dimensional latent variable therefrom from which the first machine learning system ascertains a reconstruction of the input image, and a decision is made as a function of the input image and the reconstructed input image whether or not the machine learning system is robust. 
     If the machine learning system and the neural network system are trained using datasets that include the same input images, then the monitoring is particularly reliable since it is ensured in an especially uncomplicated manner that the statistical distributions of the training datasets are comparable (i.e., identical). 
     In a still further independent aspect, the present invention relates to a method for generating an augmented training dataset which includes input images for training a machine learning system that is designed for the classification and/or semantic segmentation of input images, and latent variables are ascertained from the input images with the aid of the second machine learning system of the neural network system, the input images being classified as a function of ascertained feature characteristics of their image data, and an augmented input image of the augmented training dataset is ascertained from at least one of the input images as a function of average values of the ascertained latent variables in at least two of the classes. 
     With the aid of this method, it is possible to analyze features of the images in the space of the latent variables (latent space) and to extract disentangled features so that particularly selective variation of the features of the images is possible in the described procedure. 
     The disentanglement of the features is particularly clean if the image classes are selected in such a way that the input images (x (i) ) classified therein agree with regard to their characteristics in a predefinable set of other features. 
     In this context it may advantageously be provided that the augmented input image is ascertained with the aid of the first machine learning system of the neural network system that was trained especially using an above-mentioned training method, as a function of an ascertained augmented latent variable. On that basis, a modified image is able to be generated in an efficient manner. 
     In order to modify a predefinable feature of an existing image in a very selective manner it may be provided that the augmented latent variable be ascertained from a predefinable one of the ascertained latent variables and a difference of the average values. On that basis, the feature of the image that corresponds to the predefinable one of the ascertained latent variables is varied. 
     To obtain the greatest possible multitude of new feature characteristics, it may be provided that the different be weighted by a predefinable weighting factor α. This particularly makes it possible to generate a multitude of training images whose features are varied to different extents. 
     For street scenes, for example, it is possible to vary the visual attributes of pedestrians in a multitude of characteristics and to thereby supply an especially large training or test dataset that ensures a very high coverage with regard to this feature. 
     It may then particularly be provided that the machine learning system be trained by the generated augmented training dataset if the monitoring by one of the above-mentioned monitoring methods has revealed that the machine learning system is not robust. 
     In further aspects, the present invention relates to a computer program which is designed to execute the above methods and to a machine-readable memory medium on which this computer program is stored. 
     In the following text, embodiments of the present invention are described in greater detail with reference to the figures. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  shows schematically a structure of one embodiment of the present invention. 
         FIG. 2  shows schematically an exemplary embodiment for the control of an at least semiautonomous robot, in accordance with the present invention. 
         FIG. 3  shows schematically an exemplary embodiment for the control of a production system, in accordance with the present invention. 
         FIG. 4  shows schematically an exemplary embodiment for the control of an access system, in accordance with the present invention. 
         FIG. 5  shows schematically an exemplary embodiment for the control of a monitoring system, in accordance with the present invention. 
         FIG. 6  shows schematically an exemplary embodiment for the control of a personal assistant, in accordance with the present invention. 
         FIG. 7  shows schematically an exemplary embodiment for the control of a medical imaging system, in accordance with the present invention. 
         FIG. 8  shows a possible structure of the monitoring unit, in accordance with the present invention. 
         FIG. 9  shows a possible structure of a first training device  141 , in accordance with the present invention. 
         FIG. 10  shows the neural network system, in accordance with an example embodiment of the present invention. 
         FIG. 11  shows a possible structure of a second training device  140 , in accordance with the present invention. 
     
    
    
     DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS 
       FIG. 1  shows an actuator  10  in its environment  20  in an interaction with a control system  40 . At preferably regular time intervals, environment  20  is acquired in a sensor  30 , in particular an imaging sensor such as a video sensor, which may also be provided as a plurality of sensors, e.g., a stereo camera. Other imaging sensors such as radar, ultrasound or lidar are also possible. A thermal imaging camera is another option. Sensor signal S—or an individual sensor signal S in the case of multiple sensors—of sensor  30  is transmitted to control system  40 . Control system  40  thus receives a sequence of sensor signals S. On that basis, control system  40  ascertains actuation signals A, which are transmitted to actuator  10 . 
     Control system  40  receives the sequence of sensor signals S from sensor  30  in an optional receive unit  50 , which converts the sequence of sensor signals S into a sequence of input images x (a direct adoption of sensor signal S as input image x is possible as an alternative). Input image x, for example, may be a cutout or a further processing of sensor signal S. Input image x includes individual frames of a video recording. in other words, input image x is ascertained as a function of sensor signal S. The sequence of input images x is conveyed to a machine learning system, which is an artificial neural network  60  in the exemplary embodiment. 
     Artificial neural network  60  is preferably parameterized by parameters ϕ, which are stored in a parameter memory P and made available by this memory. 
     Artificial neural network  60  ascertains output variables y from input images x. These output variables y may particularly include a classification and/or a semantic segmentation of input images x. Output variables y are conveyed to an optional transformation unit  80 , which uses them to ascertain actuation signals A, which are conveyed to actuator  10  in order to actuate actuator  10  appropriately. Output variable y includes information about objects that were detected by sensor  30 . 
     Control system  40  furthermore includes a monitoring unit  61  for monitoring the mode of operation of artificial neural network  60 . Input image x is also conveyed to monitoring unit  61 . Monitoring unit  61  ascertains a monitoring signal d as a function thereof, which is likewise conveyed to transformation unit  80 . Actuation signal A is also ascertained as a function of monitoring signal d. 
     Monitoring signal d characterizes whether or not neural network  60  ascertains output variables y in a reliable manner. If monitoring signal d characterizes an unreliability, it may be provided, for instance, that actuation signal A be ascertained according to a protected operating mode (while it is otherwise ascertained in a normal operating mode). The protected operating mode, for instance, may include a reduction of the dynamics of actuator  10  or that functionalities for actuating actuator  10  are switched off. 
     Actuator  10  receives actuation signals A, is actuated accordingly, and carries out a corresponding action. Actuator  10  may include a (not necessarily structurally integrated) actuation logic, which ascertains a second actuation signal from actuation signal A, which will then be used to actuate actuator  10 . 
     In further embodiments, control system  40  includes sensor  30 . In still further embodiments, control system  40  alternatively or additionally also includes actuator  10 . 
     In further preferred embodiments, control system  40  includes a single processor  45  or a plurality of processors  45  and at least one machine-readable memory medium  46  on which instructions are stored that induce control system  40  to carry out the method according to the present invention when the instructions are executed on processors  45 . 
     In alternative embodiments, a display unit  10   a  is provided as an alternative or in addition to actuator  10 . 
       FIG. 2  shows the way in which control system  40  is able to be used for the control of an at least semiautonomous robot, in this case, an at least semiautonomous motor vehicle  100 . 
     Sensor  30  may be a video sensor, for instance, which is preferably situated in motor vehicle  100 . 
     Artificial neural network  60  is designed to reliably identify objects from input images x. 
     Actuator  10 , which is preferably situated in motor vehicle  100 , may be a brake, a drive or a steering system of motor vehicle  100 , for example. Actuation signal A may then be determined in such a way that actuator or actuators  10  is or are actuated in such a manner, for instance, that motor vehicle  100  prevents a collision with the objects reliably identified by artificial neural network  60 , in particular if they involve objects of certain classes such as pedestrians. 
     As an alternative, the at least semiautonomous robot may also be a different mobile robot (not shown), such as a robot which moves by flying, swimming, diving or walking. For example, the mobile robot may be an at least semiautonomous lawnmower or an at least semiautonomous cleaning robot. In these cases, as well, actuation signal A is able to be ascertained in such a way, for instance, that the drive and/or the steering system of the mobile robot is/are actuated so that the at least semiautonomous robot prevents a collision with objects identified by artificial neural network  60 . 
     Alternatively or additionally, display unit  10   a  is able to be actuated by actuation signal A and the ascertained safe regions be displayed, for instance. In a motor vehicle  100  having a non-automated steering system, for example, it is additionally possible that display unit  10   a  is actuated by actuation signal A so that it outputs an optical or acoustic warning signal when it is ascertained that motor vehicle  100  is at risk of colliding with one of the reliably identified objects. 
       FIG. 3  shows an exemplary embodiment in which control system  40  is used for actuating a production machine  11  of a production system  200  by actuating an actuator  10  that controls this production machine  11 . Production machine  11  may be a machine for punching, sawing, drilling and/or cutting, for instance. 
     For example, sensor  30  may then be an optical sensor, which detects properties of manufactured products  12   a,    12   b,  for instance. These manufactured products  12   a,    12   b  may be movable. It is possible that actuator  10 , which controls production machine  11 , is actuated as a function of an assignment of the detected manufactured products  12   a,    12   b  so that production machine  11  appropriately carries out a subsequent processing step of the correct one of manufactured products  12   a,    12   b.  It is also possible that through the identification of the correct properties of the same one of manufactured products  12   a,    12   b  (that is to say, without an incorrect assignment), production machine  11  appropriately adapts the same production step for the processing of a subsequent manufactured product. 
       FIG. 4  shows an exemplary embodiment in which control system  40  is used for the control of an access system  300 . Access system  300  may include a physical access control such as a door  401 . Video sensor  30  is set up to detect a person. With the aid of object identification system  60 , this detected image is able to be interpreted. If multiple persons are detected at the same time, for example, then an assignment of the persons (i.e., the objects) to one another makes it possible to ascertain the identity of the persons in an especially reliable manner, for instance by analyzing their movements. Actuator  10  may be a lock that releases or blocks the release of the access control, e.g., door  401 , as a function of actuation signal A. To this end, actuation signal A may be selected as a function of the interpretation by object identification system  60 , e.g., as a function of the ascertained identity of the person. Instead of the physical access control, a logical access control can be provided as well. 
       FIG. 5  shows an exemplary embodiment in which control system  40  is used for the control of a monitoring system  400 . This exemplary embodiment differs from the exemplary embodiment shown in  FIG. 5  in that display unit  10   a  is provided instead of actuator  10 , which is actuated by control system  40 . For example, artificial neural network  60  is able to reliably ascertain an identity of the objects recorded by video sensor  30 , for instance in order to infer which objects are suspicious as a function thereof, and actuation signal A may then be selected in such a way that display unit  10   a  displays this object in a color-coded, highlighted manner. 
       FIG. 6  shows an exemplary embodiment in which control system  40  is used for the control of a personal assistant  250 . Sensor  30  is preferably an optical sensor which receives images of a gesture of a user  249 . 
     Depending on the signals of sensor  30 , control system  40  ascertains an actuation signal A of personal assistant  250 , for instance in that the neural network carries out a gesture detection. This ascertained actuation signal A is conveyed to personal assistant  250 , which will then be actuated accordingly. This ascertained actuation signal A may particularly be selected so that it corresponds to an assumed desired actuation by user  249 . This assumed desired actuation is able to be ascertained as a function of the gesture identified by artificial neural network  60 . Control system  40  may then select control signal A for transmittal to personal assistant  250  as a function of the assumed desired actuation, and/or select actuation signal A for transmittal to the personal assistant according to the assumed desired actuation  250 . 
     For example, this corresponding actuation may include that personal assistant  250  calls up information from a database and reproduces it in a manner that user  249  can receive. 
     Instead of personal assistant  250 , a household appliance (not shown), especially a washing machine, a stove, an oven, a microwave unit or a dishwasher, may also be provided for a corresponding actuation. 
       FIG. 7  shows an exemplary embodiment in which control system  40  is used for the control of a medical imaging system  500  such as an MRI, x-ray or ultrasonic device. Sensor  30 , for instance, may be present in the form of an imaging sensor, and display unit  10   a  is actuated by control system  40 . For example, neural network  60  is able to ascertain whether a region recorded by the imaging sensor is abnormal, and actuation signal A may then be selected in such a way that display unit  10   a  displays this region highlighted in color. 
       FIG. 8  shows a possible structure of monitoring unit  61 . Input image x is conveyed to an encoder ENC, which uses it to ascertain what is known as a latent variable z. Latent variable z has a smaller dimensionality than input image x. This latent variable z is conveyed to a generator GEN, which uses it to generate a reconstructed image {circumflex over (x)}. In the exemplary embodiment, encoder ENC and generator GEN are provided in the form of a convolutional neural network in each case. Input image x and reconstructed image {circumflex over (x)} are conveyed to a discriminator DIS. Discriminator DIS was trained to generate the best possible variable that characterizes whether an image conveyed to discriminator DIS is a real image or whether it was generated by generator GEN. This will be described in greater detail in the further text in connection with  FIG. 10 . Generator GEN, too, is a convolutional neural network. 
     Feature maps of an l th  layer (l being a predefinable number), which result when input image x and reconstructed image {circumflex over (x)} are conveyed to generator GEN, are denoted by DIS l (x) and DIS l ({circumflex over (x)}) respectively. They are conveyed to an evaluator BE in which for example a reconstruction error E x =∥DIS l ({circumflex over (x)})−DIS l (x)∥ 2 . In an alternative embodiment (not shown), it is also possible to select the reconstruction error while circumventing discriminator DIS as E x =∥x−{circumflex over (x)}∥ 2 . 
     Next, an anomaly value A(x) is able to be ascertained as the share of the particular input images of a reference dataset (e.g., a training dataset by which discriminator DIS and/or generator GEN and/or encoder ENC was or were trained), whose reconstruction error is smaller than ascertained reconstruction error E x . If anomaly error A(x) is greater than a predefinable threshold value, then monitoring signal d is set to the value d=1, which signals that output variables y are potentially ascertained unreliably. In the other case, monitoring signal d is set to the value d=0, which signals that the ascertainment of output variables y is classified as reliable. 
       FIG. 9  shows a possible structure of a first training device  141  for training monitoring unit  51 . It is parameterized by parameters θ, which are supplied by parameter memory P. Parameters θ include generator parameters θ GEN , which parameterize generator GEN, encoding parameters θ ENC , which parameterize encoder ENC, and discriminator parameters θ DIS , which parameterize discriminator DIS. 
     Training device  141  includes a supplier  71 , which supplies input images e from a training dataset. Input images e are conveyed to monitoring unit  61  to be trained, which uses them for ascertaining output variables a. Output variables a and input images e are conveyed to an evaluator  74 , which ascertains new parameters θ′ therefrom, as described in connection with  FIG. 10 , the new parameters being transmitted to parameter memory P where they replace parameters θ. 
     The methods executed by training device  141  are able to be implemented as a computer program on a machine-readable memory medium  146  and may be carried out by a processor  145 . 
       FIG. 10  illustrates the interaction between generator GEN, encoder ENC and discriminator DIS during the training. The system of generator GEN, encoder ENC and discriminator DIS depicted here is also denoted as a neural network system in this document. 
     To begin with, discriminator DIS is trained. For instance, the following steps for training discriminator DIS may be repeated n DIS  times, n DIS  being a predefinable whole number. 
     First, a batch of real input images x is made available. They are denoted by {x (i( } i=1   m ˜p x (x) with a (generally unknown) probability distribution p x . These input images x (i)  are real images, which are supplied by a database, for instance. The totality of these input images is also referred to as a training dataset. 
     In addition, a batch of latent variables z is supplied as {z (i) } i=1   m ˜p z (z), which were randomly drawn from a probability distribution p z . Probability distribution p z  is a (multidimensional) standard normal distribution in this case. 
     A batch of random variables is furthermore supplied as {ϵ (i) } i=1   m ˜p ϵ (z), which were randomly drawn from a probability distribution p ϵ . Probability distribution p ϵ  is an equal distribution over the interval [0;1], for instance. 
     Latent variables z are conveyed to generator GEN and provide a constructed input image {circumflex over (x)}, i.e., 
         {circumflex over (x)}   (i) ←GEN( z   (i) ).
 
     Using random variable ϵ, an interpolation is carried out between input image x and constructed input image {circumflex over (x)}, i.e., 
         x   int   (i)   ←ϵx   (i) +(1−ϵ) {circumflex over (x)}   (i) .
 
     Using a predefinable gradient coefficient λ, which may be selected as λ=10, for example, a discriminator cost function 
           DIS   (i) ←DIS( {circumflex over (x)}   (i) )−DIS( x   (i) )+λ(|∇ x     int   DIS( x   int   (i) | 2 −1) 2  
 
     is then ascertained. New discriminator parameters θ DIS ′ are able to be ascertained therefrom as 
     
       
         
           
             
               
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     “Adam” representing a gradient descent method. This ends the training of discriminator DIS. 
     Generator GEN and encoder ENC are trained next. Here, too, real input images {x (i) } i=1   m ˜p x (x) and randomly selected latent variables {z (i) } i=1   m ˜p z (z) are supplied. Once again, 
         {circumflex over (x)}   (i) ←GEN( z   (i) )
 
     is ascertained. It is used for ascertaining a reconstructed latent variable {circumflex over (z)} in that constructed image {circumflex over (x)} is conveyed to encoder ENC, i.e., 
         {circumflex over (z)}   (i) ←ENC( {circumflex over (x)}   (i) ).
 
     In the same way, as illustrated in  FIG. 8 , an attempt is made to reconstruct input image x with the aid of encoder ENC and generator GEN, i.e., 
         {circumflex over (x)}   (i) ←GEN(ENC( x   (i) )).
 
     A generator cost function    GEN   (i) , a reconstruction cost function    recon     x     (i)  of input image x and a reconstruction cost function    recon     z     (i)  of latent variable z are then ascertained as 
           GEN   (i) ←DIS( {circumflex over (x)}   (i) )
 
           recon     x     (i) ←∥DIS l ( x   (i) )−DIS l ( {circumflex over (x)}   (i) )∥ 2   2  
 
           recon     z     (i)   ←∥z   (i)   −{circumflex over (z)}   (i) ∥ 2   2 .
 
     New generator parameters θ GEN ′ and new encoder parameters θ ENC ′ are then ascertained as 
     
       
         
           
             
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     New generator parameters θ GEN ′, new encoder parameters θ ENC ′, and new discriminator parameters θ DIS ′ then replace generator parameters θ GEN , encoder parameters θ ENC  and discriminator parameters θ DIS . 
     At this point, a convergence of parameters θ is able to be checked, and the training of discriminator DIS and/or of generator GEN and encoder ENC possibly be repeated until a convergence is achieved. This ends the present method. 
       FIG. 11  shows an exemplary second training device  140  for training neural network  60 . Training device  140  includes a supplier  72 , which supplies input images x and setpoint output variables ys, such as setpoint classifications. Input image x is conveyed to artificial neural network  60  to be trained, which uses it to ascertain output variables y. Output variables y and setpoint output variables ys are conveyed to a comparer  75 , which ascertains new parameters ϕ′ therefrom as a function of an agreement between respective output variables y and setpoint output variables ys, the new parameters being transmitted to parameter memory P where they replace parameters θ. 
     The methods executed by training system  140  may be implemented as a computer program on a machine-readable memory medium  148  and executed by a processor  147 . 
     A dataset that includes input images x and associated setpoint output variables ys may be augmented or generated (e.g., by supplier.  72 ) in the following manner. To begin with, a dataset including input images x (i)  is made available. They are classified according to predefinable characteristics (denoted as “A” and “B” by way of example) of a feature. For instance, vehicles may be classified according to the feature ‘headlights switched on’ or ‘headlights switched off’, or identified cars may be classified according to the type “passenger car” or “station wagon”. Also, different characteristics of the feature “hair color” for detected pedestrians are possible. Depending on the particular characteristic of this feature, the input images x (i)  are subdivided into two sets, i.e., I A {i|x (i)  has the characteristic “A”} and I B ={i|x (i)  has the characteristic “B”}. In an advantageous manner, these sets furthermore are homogenized to the effect that input images x (i)  have the same characteristic X for a predefinable set of other features, preferably of all other features, that is to say, 
       I A ←I A ∩{i|x (i)  has the characteristic X}
 
       I B ←I B ∩{i|x (i)  has the characteristic X}.
 
     With the aid of encoder ENC, associated latent variables z (i) =ENC(x (i) ) are then ascertained for each one of input images x (i) . 
     The average values of the latent variables are ascertained across the sets, i.e., 
     
       
         
           
             
               
                 
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     Next, the differences of the average values are formed, i.e., 
         v   A−B   = z     B   − z     A . 
     For images from set I A , new latent variables are now formed with a predefinable scale factor α, which may assume values between 0 and 1, for instance, that is to say, 
         z   new   (i)   =z   (i)   +α*v   A−B   , i∈I   A . 
     Accordingly, new latent variables are able to be formed for images from the set I B  as 
         z   new   (i)   =z   (i)   −α*v   A−B   , i∈I   B . 
     From this, new images x new   (i)  are able to be generated by 
         x   new   (i) ←ENC( z   new   (i) ).
 
     It is of course not necessary to classify whole images. It is possible, for example, to use a detection algorithm for classifying image segments as objects, that these image segments are then cut out, a new image segment (according to new image x new   (i) ) is generated as the case may be, and is inserted into the associated image in place of the cut-out image segment. For example, this makes it possible to selectively adapt the hair color of a pedestrian in an image featuring this detected pedestrian. 
     With the exception of the classification of the feature varied in this way between characteristic “A” and “B”, associated setpoint output variable ys may be adopted in unchanged form. This makes it possible to generate the augmented dataset and to train neural network  60  with this augmented dataset. The method then ends. 
     The term ‘computer’ encompasses various devices for processing predefinable processing rules. These processing rules may be present in the form of software or in the form of hardware or also in a mixed form of software and hardware.