Device and computer-implemented methods for machine learning, for providing a machine learning system, or for operating a technical system

A computer-implemented method for providing a machine learning system including a neural network for semantic segmentation, or object recognition, or classification of objects, the method comprising the following steps:

• generating a synthetic digital image by:
• providing a latent variable of a diffusion model representing the synthetic digital image, wherein the providing of the latent variable includes:
• (i) sampling the latent variable from random noise, or (ii) providing a digital reference image, and adding noise with the diffusion model to the digital reference image to determine the latent variable, or (iii) mapping the digital reference image with an encoder to an input of the diffusion model, and adding noise with the diffusion model to the input to determine the latent variable, and
• mapping the latent variable with the diffusion model depending on parameters of the diffusion model to features of the diffusion model, mapping the features with the diffusion model depending on parameters of the diffusion model to an output of the diffusion model, wherein the synthetic digital image comprises the output;
• and
• training or testing or verifying or validating the machine learning system for semantic segmentation or object recognition or classification of objects, depending on the synthetic digital image.

A computer-implemented method for operating a technical system, the technical system including a computer-controlled machine the method comprising the following steps:

• providing a machine learning system by:
• generating a synthetic digital image by:
• providing a latent variable of a diffusion model representing the synthetic digital image, wherein the providing of the latent variable includes:
• (i) sampling the latent variable from random noise, or (ii) providing a digital reference image, and adding noise with the diffusion model to the digital reference image to determine the latent variable, or (iii) mapping the digital reference image with an encoder to an input of the diffusion model, and adding noise with the diffusion model to the input to determine the latent variable, and
• mapping the latent variable with the diffusion model depending on parameters of the diffusion model to features of the diffusion model, mapping the features with the diffusion model depending on parameters of the diffusion model to an output of the diffusion model, wherein the synthetic digital image comprises the output, and
• training or testing or verifying or validating the machine learning system for semantic segmentation or object recognition or classification of objects, depending on the synthetic digital image;
• capturing a digital image with a sensor, the digital image including a video image or a radar image or a LiDAR image or an ultrasonic image or a motion image or a thermal image;
• and
• determining a control signal for operating the technical system with the machine learning system, depending on the digital image.

A device, comprising:

• at least one processor;
• and
• at least one memory;
• wherein the at least one processor is configured to execute instructions for generating a synthetic digital image:
• (i) for generating training data and/or test data for training a machine learning system, or (ii) for training an image classifier, the instruction, when executed by the at least one processor, causing the device to perform the following steps:
• providing a latent variable of a diffusion model representing the synthetic digital image, wherein the providing of the latent variable includes:
• (i) sampling the latent variable from random noise, or (ii) providing a digital reference image, and adding noise with the diffusion model to the digital reference image to determine the latent variable, or (iii) mapping the digital reference image with an encoder to an input of the diffusion model, and adding noise with the diffusion model to the input to determine the latent variable;
• and
• (i) mapping the latent variable with the diffusion model depending on parameters of the diffusion model to features of the diffusion model, mapping the features with the diffusion model depending on parameters of the diffusion model to an output of the diffusion model, wherein the synthetic digital image comprises the output, or (ii) mapping the output with a decoder to the synthetic digital image, mapping the features or the synthetic digital image with a discriminator to a class of a predetermined set of classes for a real digital image or to a class for a synthetic digital image, depending on parameters of the discriminator, and learning at least one parameter of the diffusion model and/or the discriminator depending on a loss for the discriminator that is defined depending on the class and a predetermined reference class;
• wherein the at least one memory is configured to store the instructions.

A non-transitory computer-readable medium on which is stored a computer program including computer-readable instructions for generating a synthetic digital image:

• (i) for generating training data and/or test data for training a machine learning system, or (ii) for training an image classifier, the instruction, when executed by a computer, causing the computer to perform the following steps:
• providing a latent variable of a diffusion model representing the synthetic digital image, wherein the providing of the latent variable includes:
• (i) sampling the latent variable from random noise, or (ii) providing a digital reference image, and adding noise with the diffusion model to the digital reference image to determine the latent variable, or (iii) mapping the digital reference image with an encoder to an input of the diffusion model, and adding noise with the diffusion model to the input to determine the latent variable;
• and
• (i) mapping the latent variable with the diffusion model depending on parameters of the diffusion model to features of the diffusion model, mapping the features with the diffusion model depending on parameters of the diffusion model to an output of the diffusion model, wherein the synthetic digital image comprises the output, or (ii) mapping the output with a decoder to the synthetic digital image, mapping the features or the synthetic digital image with a discriminator to a class of a predetermined set of classes for a real digital image or to a class for a synthetic digital image, depending on parameters of the discriminator, and learning at least one parameter of the diffusion model and/or the discriminator depending on a loss for the discriminator that is defined depending on the class and a predetermined reference class.