The present disclosure relates to a method for training a deep neural network (30) for object recognition in the surroundings of a motor vehicle (1). A deep neural network (30) is trained using a number of training cycles (101), wherein a number of consecutive training iteration steps and a subsequent validation step are performed with each training cycle (101). On the basis of training data sets and by minimising a cost function, a number of weighting parameters including a number of prototypes (p) of the deep neural network (30) are adjusted in the training iteration steps. A number of predefined validation data sets are specified in each validation step for which the deep neural network (30) generates a latent representative data set (Z). Relevant latent representations (zk, l) are selected and stored as annotated data for each validation data set. After a training cycle (101), a distance (ak, l) is determined between the relevant latent representations (zk, l) of the current validation step and of a previous validation step which are saved to a validation data set. A mean distance value (a̅) is calculated from the determined distances. Training of the deep neural network (30) is terminated as soon as the calculated mean distance value (a̅) reaches a stability threshold (S3).


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    Title :

    VERFAHREN ZUM TRAINIEREN EINES TIEFEN NEURONALEN NETZES


    Additional title:

    METHOD FOR TRAINING A DEEP NEURAL NETWORK
    PROCÉDÉ DE FORMATION D'UN RÉSEAU NEURONAL PROFOND


    Contributors:

    Publication date :

    2024-05-22


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    German


    Classification :

    IPC:    G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS



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