Robert Boschgmbh

Robert Bosch Gmbh

Vadim Sushko

Anna Khoreva

Dan Zhang

Edgar Schoenfeld

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 generative adversarial network. The generative adversarial network includes: a generator configured for generating an image and a corresponding label map; a discriminator configured for determining a classification of a provided image and a provided label map, wherein the classification characterizes whether the provided image and the provided label map have been generated by the generator or not and determining the classification comprises the steps of: determining a first feature map of the provided image; masking the first feature map according to the provided label map thereby determining a masked feature map; globally pooling the masked feature map thereby determining a feature representation of the provided image masked by the provided label map; determining a classification of the image based on the feature representation.

Generative adversarial network for classifying an image by masking and globally pooling a feature map

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.

Training a generator for generating realistic images using a semantically segmenting discriminator

A computer-implemented method for training a generative adversarial network. A generator of the generative adversarial network is configured to generate at least one image based on at least one input value. Training the generative adversarial network includes maximizing a loss function that characterizes a difference between a first image determined by the generator for at least one first input value and a third image determined by the generator for at least one second input value.

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Stuttgart, Germany

Vadim Sushko