Robert Boschgmbh

Robert Bosch Gmbh

Claudia Blaiotta

Volker Fischer

Andres Mauricio Munoz Delgado

Piyapat Saranrittichai

Chaithanya Kumar Mummadi

Sebastian Ziesche

A method for generating, from an input image, an output image that a given image classifier classifies into a target class chosen from multiple available classes of a given classification. The method includes mapping, using a trained encoder network, the input image to a lower dimensional representation in a latent space; drawing a noise sample from a given distribution; and mapping, using a trained generator network, the noise sample to an output image, wherein this mapping is conditioned both on the target class and on the representation.

Data augmentation for the training of image classifiers

A method for training a neural network for determining a task output with respect to a given task. The method includes: providing unlabeled and/or labelled encoder training records of measurement data; training the encoder network to map encoder training records to representations towards the goal that these representations, and/or or one or more work products derived from the representations, fulfil a self-consistency condition or correspond to ground truth; providing task training records that are labelled with ground truth; and training the association network and the task head networks towards the goal that, when a task training record is mapped to a representation using the encoder network, and the representation is mapped to a task output by the combination of the association network and the task head networks, the so-obtained task output corresponds to the ground truth with which the training record is labelled, as measured by a task loss function.

Training neural networks with a lesser requirement for labelled training data

A computer-implemented prediction method of making time-series predictions for controlling and/or monitoring a computer-controlled system, such as a semi-autonomous vehicle. The method uses a time series of one or more observed states. A state comprises values of measurable quantities of multiple interacting objects. Based on the observed states, values of time-invariant latent features for the multiple objects are determined, for example, according to an encoder model. A decoder model is then used to predict at least one next state. This involves applying a trained graph model to obtain a first prediction contribution based on an object's interactions with other objects, and applying a trained function to obtain a second prediction contribution based just on information about the object itself. Based on the predicted next state, output data is generated for use in controlling and/or monitoring the computer-controlled system.

Making time-series predictions using a trained decoder model

A prediction device is described for predicting a location of a pedestrian moving in an environment. The prediction device may have a memory configured to store a probability distribution for multiple latent variables indicating one or more states of the one or more pedestrians. The prediction device may be configured to predict a position of a pedestrian for which no position information is currently available from the probability distribution of the multiple latent variables.

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Stuttgart, Germany

Claudia Blaiotta