Invention Grant
- Patent Title: Generative adversarial networks for image segmentation
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Application No.: US17263813Application Date: 2019-06-07
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Publication No.: US11935243B2Publication Date: 2024-03-19
- Inventor: Michael Hofmann , Nora Baka , Cedric Nugteren , Mohsen Ghafoorian , Olaf Booij
- Applicant: TomTom Global Content B.V.
- Applicant Address: NL Amsterdam
- Assignee: TomTom Global Content B.V.
- Current Assignee: TomTom Global Content B.V.
- Current Assignee Address: NL Amsterdam
- Agency: Park, Vaughan, Fleming & Dowler LLP
- Priority: GB 09604 2018.06.12
- International Application: PCT/EP2019/064945 2019.06.07
- International Announcement: WO2019/238560A 2019.12.19
- Date entered country: 2021-01-27
- Main IPC: G06T7/11
- IPC: G06T7/11 ; G06F18/214 ; G06N3/045 ; G06N3/08 ; G06V10/764 ; G06V10/82 ; G06V20/10 ; G06V20/56 ; G06V20/70

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.
Public/Granted literature
- US20210303925A1 Generative Adversarial Networks for Image Segmentation Public/Granted day:2021-09-30
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