Robert Boschgmbh

Robert Bosch Gmbh

Anna Khoreva

Dan Zhang

Edgar Schoenfeld

Vadim Sushko

Max Welling

Thomas Andy Keller

Priyank Jaini

Thomas Binzer

Juergen Hasch

Volker Fischer

Andres Mauricio Munoz Delgado

Jorn Peters

Julio Borges

Emiel Hoogeboom

Prateek Katiyar

Lukas Hoyer

Yumeng Li

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

Device and method for training an artificial neural network, including providing a neural network layer for an equivariant feature mapping having a plurality of output channels, grouping channels of the output channels into a number of distinct groups, wherein the output channels of each individual distinct group are organized into an individual grid defining a spatial location of each of the output channels of the individual distinct group in the grid for the individual distinct group, providing for each of the output channels of each individual distinct group, a distinct normalization function which is defined depending on the spatial location of the output channel in the grid in that this output channel is organized and depending on tunable hyperparameters for the normalization function, determining an output of the artificial neural network depending on a result of each of the distinct normalization functions, training the hyperparameters of the artificial neural network.

Device and computer implemented method for training an artificial neural network using channel groups

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 training method for training a generator neural network configured to generate synthesized sensor data. A fidelity destroying transformation is defined configured to transform a measured sensor data to obtain a fidelity-destroyed transformed measured sensor data. A fidelity preserving transformation is defined configured to transform a measured sensor data to obtain a fidelity-preserved transformed measured sensor data.

Training method for a generator neural network imposing data equivariances

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.

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

A method for training a neural network. The neural network comprises a first layer which includes a plurality of filters to provide a first layer output comprising a plurality of feature maps. Training of the classifier includes: receiving, by a preceding layer, a first layer input in the first layer, wherein the first layer input is based on the input signal; determining the first layer output based on the first layer input and a plurality of parameters of the first layer; determining a first layer loss value based on the first layer output, wherein the first layer loss value characterizes a degree of dependency between the feature maps, the first layer loss value being obtained in an unsupervised fashion; and training the neural network. The training includes an adaption of the parameters of the first layer, the adaption being based on the first layer loss value.

Device and method for training a neuronal network

A method for generating synthetic measurement data indistinguishable from actual measurement data captured by a first physical measurement modality. The first physical measurement modality is based on emitting an interrogating wave towards an object and recording a reflected wave coming from the object in a manner that allows for a determination of the time-of-flight between the emission of the interrogating beam and the arrival of the reflected wave. The method includes: obtaining a first compressed representation of the synthetic measurement data in a first latent space, wherein this first latent space is associated with a first decoder that is trained to map each element of the first latent space to a record of synthetic measurement data that is indistinguishable from records of actual measurement data of the first physical measurement modality, and applying the first decoder to the first compressed representation, so as to obtain the sought synthetic measurement data.

Synthetic generation of radar, LIDAR and ultrasound measurement data

A computer-implemented method for training an augmented discriminator and a generator. The method includes: providing a training set comprising real training samples and artificial training samples for training of the augmented discriminator, wherein the artificial training samples are generated by the generator; assigning a data sequence to at least one data sample of the training set; wherein each pair of data sample and assigned data sequence is assigned to one of a plurality of classes such that, given the assigned one class and the assigned data sequence characterize whether the data sample is a real training sample or an artificial training sample; training the augmented discriminator to compute from pairs of data sample and assigned data sequence the respective class to which the corresponding pair is assigned; training the generator to generate artificial training samples such that the augmented discriminator is not able to correctly compute the aforementioned one class.

Device and method for training an augmented discriminator

A computer-implemented method for training a normalizing flow. The normalizing flow predicts a first density value based on a first input image. The first density value characterizes a likelihood of the first input image to occur. The first density value is predicted based on an intermediate output of a first convolutional layer of the normalizing flow. The intermediate output is determined based on a plurality of weights of the first convolutional layer. The method for training includes: determining a second input image; determining an output, wherein the output is determined by providing the second input image to the normalizing flow and providing an output of the normalizing flow as output; determining a second density value based on the output tensor and on the plurality of weights; determining a natural gradient of the plurality of weights with respect to the second density value; adapting the weights according to the natural gradient.

Device and method for training a normalizing flow

A computer-implemented method of training a machine learnable function, such as an image classifier or image feature extractor. When applying such machine learnable functions in autonomous driving and similar application areas, generalizability may be important. To improve generalizability, the machine learnable function is rewarded for responding predictably at a layer of the machine learnable function to a set of differences between input observations. This is done by means of a regularization objective included in the objective function used to train the machine learnable function. The regularization objective rewards a mutual statistical dependence between representations of input observations at the given layer, given a difference label indicating a difference between the input observations.

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Anna Khoreva