Robert Boschgmbh

Nianlong Gu

Lydia Gauerhof

A computer-implemented neural network system including a first machine learning system, in particular a first neural network, a second machine learning system, in particular a second neural network, and a third machine learning system, in particular a third neural network. The first machine learning system is designed to ascertain a higher-dimensional constructed image from a predefinable low-dimensional latent variable. The second machine learning system is designed to ascertain the latent variable again from the higher-dimensional constructed image, and the third machine learning system is designed to distinguish whether or not an image it receives is a real image.

Method and device for testing the robustness of an artificial neural network

A method for training a machine learning system. The method includes generating an augmented dataset including input images for training the machine learning system, which is for classification and/or semantic segmentation of input images, using a first machine learning system, which is embodied as a decoder of an autoencoder, and a second machine learning system, which is embodied as an encoder of the autoencoder. Latent variables are ascertained from the input images using the encoder. The input images are classified as a function of ascertained feature characteristics of their image data. An augmented input image of the augmented dataset is ascertained from at least one of the input images as a function of average values of the ascertained latent variables in at least two of the classes. The image classes are selected so that the input images classified therein agree in their characteristics in a predefinable set of other features.

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Wettingen, Switzerland

Nianlong Gu