The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Mar. 19, 2024

Filed:

Jun. 07, 2019
Applicant:

Tomtom Global Content B.v., Amsterdam, NL;

Inventors:

Michael Hofmann, Amsterdam, NL;

Nora Baka, Delft, NL;

Cedric Nugteren, Amsterdam, NL;

Mohsen Ghafoorian, Diemen, NL;

Olaf Booij, Leiden, NL;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/11 (2017.01); G06F 18/214 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/10 (2022.01); G06V 20/56 (2022.01); G06V 20/70 (2022.01);
U.S. Cl.
CPC ...
G06T 7/11 (2017.01); G06F 18/214 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/10 (2022.01); G06V 20/56 (2022.01); G06V 20/588 (2022.01); G06V 20/70 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30256 (2013.01);
Abstract

A method is provided of training a generative adversarial network for performing semantic segmentation of images. The generative adversarial network includes a generator neural network and a discriminator neural network. The method includes providing an image as input to the generator neural network, receiving a predicted segmentation map for the image from the generator neural network, providing i) the image, ii) the predicted segmentation map, and iii) ground-truth label data corresponding to the image, as distinct training inputs to the discriminator neural network, determining a set of one or more outputs from the discriminator neural network in response to said training inputs, and training the generator neural network using a loss function that is a function of said set of outputs from the discriminator neural network.


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