Robert Boschgmbh

Robert Bosch Gmbh

Edgar Schoenfeld

Anna Khoreva

Vadim Sushko

Dan Zhang

Julio Borges

A device and computer-implemented method for determining pixels of a synthetic image. The method comprises providing a generator that is configured to determine an output from a first input comprising a label map and a first latent code, wherein the label map comprises a mapping of at least one class to at least one of the pixels, wherein the method comprises providing the label map and a latent code, wherein the latent code comprises input data points in a latent space, providing a first direction for moving input data points in the latent space, determining the first latent code depending on at least one input data point of the latent code that is moved in the first direction, determining the synthetic image depending on an output of the generator for the first input.

Device and computer-implemented method for determining pixels of a synthetic image

A method for generating images from a semantic map, which assigns to each pixel of the images a semantic meaning of an object to which this pixel belongs. The semantic map is provided as a map tensor comprising channels which each indicates all the pixels of the images to be generated, to which the semantic map assigns a specific semantic meaning; a set of variable pixels of the images to be generated is provided, which are to vary from one image to the next; using values taken from a random distribution, a noise tensor with channels is generated, those values of the noise tensor which relate to the set of variable pixels being reused for each image to be generated; the channels of the map tensor are merged with the channels of the noise tensor to yield an input tensor, which is mapped by a trained generator onto an image.

Generating realistic images from specified semantic maps

A method for training a generator for images from a semantic map that assigns each pixel of the image a semantic meaning of an object to which that pixel belongs. In the method, a mixed image is generated from an image generated by the generator and a determined actual training image, in which mixed image a first genuine subset of pixels is occupied by relevant corresponding pixel values of the image generated by the generator and the remaining genuine subset of pixels is occupied by relevant corresponding pixel values of the actual training image; and the images generated by the generator, the actual training image, and at least one mixed image, which belong to the same semantic training map, are supplied to a discriminator, which is configured to distinguish images generated by the generator from actual images of the scenery predefined by the semantic training map.

Training method for a generator for generating realistic images

A device and method for evaluating a control of a generator for determining pixels of a synthetic image. The generator determining pixels of the synthetic image from a first input comprising a label map and a first latent code. The method includes providing the label map and latent code which includes input data points in a latent space; providing the control including a set of directions for moving the latent code in the latent space, determining the first latent code depending on at least one input data point of the latent code that is moved in a first direction which is selected from the set of directions, determining a distance between at least one pair of synthetic images generated by the generator for different first inputs which comprise the label map and vary by the first direction that is selected for determining the first latent code from the latent code.

Device and computer-implemented method for evaluating a control of a generator for determining pixels of a synthetic image

A computer-implemented method for training a first machine learning system which is configured to generate an output characterizing a label map of an image. The method includes: providing first and second inputs, the first input characterizing a binary vector characterizing respective presences or absences of classes from a plurality of classes, and the second input characterizing a randomly drawn value; determining, by a first generator, an output based on the first and second inputs, the output characterizing a first label map, wherein the first label map characterizes probabilities for the classes from the plurality of classes; determining a representation of the first label map using a global pooling operation; training the first machine learning system based on a loss function, wherein the loss function characterizes an F1 loss, wherein the F1 loss characterizes a difference between the first input and the representation of the first label map.

Device and method for generating training data for a machine learning system

A method for training a generator for images from a semantic map that assigns to each pixel of the image a semantic meaning of an object to which this pixel belongs. The images generated by the generator and the at least one real training image that belong to the same semantic training map are supplied to a discriminator. The discriminator ascertains a semantic segmentation of the image assigned to it, the segmentation assigning a semantic meaning to each pixel of this image. From the semantic segmentation ascertained by the discriminator, it is evaluated whether the image supplied to the discriminator is a generated image or a real training image.

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Tuebingen, Germany

Edgar Schoenfeld