US 12,093,345 B2
Generator networks for generating images with predetermined counts of objects
Amrutha Saseendran, Renningen (DE); Kathrin Skubch, Frankfurt (DE); and Margret Keuper, Mannheim (DE)
Assigned to ROBERT BOSCH GMBH, Stuttgart (DE)
Filed by Robert Bosch GmbH, Stuttgart (DE)
Filed on Aug. 19, 2021, as Appl. No. 17/445,440.
Claims priority of application No. 20199265 (EP), filed on Sep. 30, 2020.
Prior Publication US 2022/0101056 A1, Mar. 31, 2022
Int. Cl. G06F 18/214 (2023.01); G06F 18/2132 (2023.01); G06F 18/2413 (2023.01); G06F 18/28 (2023.01); G06N 3/045 (2023.01); G06N 3/088 (2023.01)
CPC G06F 18/2148 (2023.01) [G06F 18/2132 (2023.01); G06F 18/2413 (2023.01); G06F 18/28 (2023.01); G06N 3/045 (2023.01); G06N 3/088 (2013.01)] 14 Claims
OG exemplary drawing
 
1. A method for training a generator network that is configured to generate images with multiple objects, comprising the following steps:
providing a set of training images and, for each respective training image of the training images, at least one actual count of objects that the respective training image contains;
providing a generator network that is configured to map a combination of a noise sample and at least one target count of objects to a generated image;
providing a discriminator network that is configured to map an image to a combination of: a decision of whether the image is a training image or a generated image produced by the generator network, and at least one predicted count of objects in the image;
drawing noise samples and target counts of objects;
mapping, by the generator network, the noise samples and target counts of objects to generated images;
randomly drawing images from a pool including the generated images and the training images;
supplying the randomly drawn images to the discriminator network, the discriminator network mapping each respective drawn image of the randomly drawn images to a combination of: a decision whether the respective drawn image is a training image or a generated image, and at least one predicted count of objects in the respective drawn image;
optimizing discriminator parameters that characterize a behavior of the discriminator network with a goal of improving an accuracy with which the discriminator network distinguishes between generated images and training images;
optimizing generator parameters that characterize a behavior of the generator network with a goal of deteriorating the accuracy; and
further optimizing both the generator parameters and the discriminator parameters with a goal of improving a match between the predicted count of objects on the one hand, and the actual count or the target count of objects on the other hand.