- 专利标题: Generative adversarial networks for image segmentation
-
申请号: US17263813申请日: 2019-06-07
-
公开(公告)号: US11935243B2公开(公告)日: 2024-03-19
- 发明人: Michael Hofmann , Nora Baka , Cedric Nugteren , Mohsen Ghafoorian , Olaf Booij
- 申请人: TomTom Global Content B.V.
- 申请人地址: NL Amsterdam
- 专利权人: TomTom Global Content B.V.
- 当前专利权人: TomTom Global Content B.V.
- 当前专利权人地址: NL Amsterdam
- 代理机构: Park, Vaughan, Fleming & Dowler LLP
- 优先权: GB 09604 2018.06.12
- 国际申请: PCT/EP2019/064945 2019.06.07
- 国际公布: WO2019/238560A 2019.12.19
- 进入国家日期: 2021-01-27
- 主分类号: G06T7/11
- IPC分类号: G06T7/11 ; G06F18/214 ; G06N3/045 ; G06N3/08 ; G06V10/764 ; G06V10/82 ; G06V20/10 ; G06V20/56 ; G06V20/70
摘要:
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.
公开/授权文献
- US20210303925A1 Generative Adversarial Networks for Image Segmentation 公开/授权日:2021-09-30
信息查询