Invention Grant
- Patent Title: Sparse convolutional neural networks
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Application No.: US17363986Application Date: 2021-06-30
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Publication No.: US11860629B2Publication Date: 2024-01-02
- Inventor: Raquel Urtasun , Mengye Ren , Andrei Pokrovsky , Bin Yang
- Applicant: UATC, LLC
- Applicant Address: US CA Mountain View
- Assignee: UATC, LLC
- Current Assignee: UATC, LLC
- Current Assignee Address: US CA Mountain View
- Agency: Dority & Manning, P.A.
- Main IPC: G05D1/00
- IPC: G05D1/00 ; G01S17/89 ; G01S17/86 ; G01S17/931 ; G05D1/02

Abstract:
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.
Public/Granted literature
- US20210325882A1 Sparse Convolutional Neural Networks Public/Granted day:2021-10-21
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