- 专利标题: Sparse convolutional neural networks
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申请号: US17363986申请日: 2021-06-30
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公开(公告)号: US11860629B2公开(公告)日: 2024-01-02
- 发明人: Raquel Urtasun , Mengye Ren , Andrei Pokrovsky , Bin Yang
- 申请人: UATC, LLC
- 申请人地址: US CA Mountain View
- 专利权人: UATC, LLC
- 当前专利权人: UATC, LLC
- 当前专利权人地址: US CA Mountain View
- 代理机构: Dority & Manning, P.A.
- 主分类号: G05D1/00
- IPC分类号: G05D1/00 ; G01S17/89 ; G01S17/86 ; G01S17/931 ; G05D1/02
摘要:
The present disclosure provides systems and methods that apply neural networks such as, for example, convolutional neural networks, to sparse imagery in an improved manner. For example, the systems and methods of the present disclosure can be included in or otherwise leveraged by an autonomous vehicle. In one example, a computing system can extract one or more relevant portions from imagery, where the relevant portions are less than an entirety of the imagery. The computing system can provide the relevant portions of the imagery to a machine-learned convolutional neural network and receive at least one prediction from the machine-learned convolutional neural network based at least in part on the one or more relevant portions of the imagery. Thus, the computing system can skip performing convolutions over regions of the imagery where the imagery is sparse and/or regions of the imagery that are not relevant to the prediction being sought.
公开/授权文献
- US20210325882A1 Sparse Convolutional Neural Networks 公开/授权日:2021-10-21
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