Device and computer-implemented method for determining a data set for use in training, for training and for operating a machine learning system

A device configured to determine a data set for use in training a machine learning system, the device comprising:

• at least one processor;
• and
• at least one memory, wherein the at least one memory stores instructions determining the data set for use in the training of the machine learning system, the instructions, when executed by the at least one processor, causing the at least one processor to perform:
• combining at least two data sets to form the data set for use in training, wherein the at least two data sets include a first data set and a second data set, wherein the first data set includes a first digital image which is identified by a one-hot encoding with one label or by a multi-hot encoding with a plurality of labels, from a first set of labels, and the second data includes a second digital image which is identified by a one-hot encoding with one label or by a multi-hot encoding with a plurality of labels, from a second set of labels, wherein the first set of labels and the second set of labels differ by at least one label, wherein, depending on the first set of labels and the second set of labels, a first encoding for identifying the first digital image is determined with labels from the first set of labels and the second set of labels, and the first encoding is mapped to a first representation in a state space, wherein the data set for use in training includes the first digital image and the first representation.

A device for training a machine learning system, comprising:

• at least one processor;
• and
• at least one memory, wherein the at least one memory stores instructions for training the machine learning system, the instructions, when executed by the at least one processor, causing the at least one processor to train the machine learning model using a determined data set for use in training, the data set for use in training having been determined by:
• combining at least two data sets to form the data set for use in training, wherein the at least two data sets include a first data set and a second data set, wherein the first data set includes a first digital image which is identified by a one-hot encoding with one label or by a multi-hot encoding with a plurality of labels, from a first set of labels, and the second data includes a second digital image which is identified by a one-hot encoding with one label or by a multi-hot encoding with a plurality of labels, from a second set of labels, wherein the first set of labels and the second set of labels differ by at least one label, wherein, depending on the first set of labels and the second set of labels, a first encoding for identifying the first digital image is determined with labels from the first set of labels and the second set of labels, and the first encoding is mapped to a first representation in a state space, wherein the data set for use in training includes the first digital image and the first representation;
• wherein the machine learning system includes a model:
• configured to map at least one label including a one-hot encoding of a label or a multi-hot encoding of a plurality of labels, to a representation of the at least one label in the state space provided in the data set for use in training, and to map the representation of the at least one label from the state space to a synthetic digital image, wherein the model is trained with the first representation and the first digital image from the data set and/or with the second representation and the second digital image from the data set, to map the representation from the state space to the synthetic digital image, or
• configured to map a digital image to a representation of at least one label in the state space provided in the data set for use in training and to determine the representation of the at least one label from the state space to the at least one label including a one-hot encoding of one label or a multi-hot encoding of a plurality of labels, for the digital image, wherein the model is trained with the first representation and the first digital image from the data set and/or with the second representation and the second digital image from the data set, to map the digital image to the representation in the state space.

A device for operating a machine learning system, the device comprising:

• at least one processor;
• and
• at least one memory, wherein the at least one memory which store instructions for operating a trained machine learning system, the machine learning model having been trained using a determined data set for use in training, the data set for use in training having been determined by:
• combining at least two data sets to form the data set for use in training, wherein the at least two data sets include a first data set and a second data set, wherein the first data set includes a first digital image which is identified by a one-hot encoding with one label or by a multi-hot encoding with a plurality of labels, from a first set of labels, and the second data includes a second digital image which is identified by a one-hot encoding with one label or by a multi-hot encoding with a plurality of labels, from a second set of labels, wherein the first set of labels and the second set of labels differ by at least one label, wherein, depending on the first set of labels and the second set of labels, a first encoding for identifying the first digital image is determined with labels from the first set of labels and the second set of labels, and the first encoding is mapped to a first representation in a state space, wherein the data set for use in training includes the first digital image and the first representation;
• wherein the machine learning system includes a model:
• configured to map at least one label including a one-hot encoding of a label or a multi-hot encoding of a plurality of labels, to a representation of the at least one label in the state space provided in the data set for use in training, and to map the representation of the at least one label from the state space to a synthetic digital image, wherein the model is trained with the first representation and the first digital image from the data set and/or with the second representation and the second digital image from the data set, to map the representation from the state space to the synthetic digital image, or
• configured to map a digital image to a representation of at least one label in the state space provided in the data set for use in training and to determine the representation of the at least one label from the state space to the at least one label including a one-hot encoding of one label or a multi-hot encoding of a plurality of labels, for the digital image, wherein the model is trained with the first representation and the first digital image from the data set and/or with the second representation and the second digital image from the data set, to map the digital image to the representation in the state space;
• wherein the operating of the trained machine learning system includes operating the trained machine learning system:
• with the model, the at least one label is mapped to the representation of the at least one label in the state space and the representation of the at least one label in the state space is mapped to the synthetic digital image, or
• with the model, the at least one label for the digital image is determined.