Invention Grant
- Patent Title: Transforming convolutional neural networks for visual sequence learning
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Application No.: US17325024Application Date: 2021-05-19
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Publication No.: US11645530B2Publication Date: 2023-05-09
- Inventor: Xiaodong Yang , Pavlo Molchanov , Jan Kautz
- Applicant: NVIDIA Corporation
- Applicant Address: US CA Santa Clara
- Assignee: NVIDIA Corporation
- Current Assignee: NVIDIA Corporation
- Current Assignee Address: US CA Santa Clara
- Agency: Leydig, Voit and Mayer, Ltd.
- Main IPC: G06N3/082
- IPC: G06N3/082 ; G06V20/40 ; G06V10/764 ; G06V10/82 ; G06F18/24 ; G06N3/044 ; G06N3/045 ; G06N3/048

Abstract:
A method, computer readable medium, and system are disclosed for visual sequence learning using neural networks. The method includes the steps of replacing a non-recurrent layer within a trained convolutional neural network model with a recurrent layer to produce a visual sequence learning neural network model and transforming feedforward weights for the non-recurrent layer into input-to-hidden weights of the recurrent layer to produce a transformed recurrent layer. The method also includes the steps of setting hidden-to-hidden weights of the recurrent layer to initial values and processing video image data by the visual sequence learning neural network model to generate classification or regression output data.
Public/Granted literature
- US20210271977A1 TRANSFORMING CONVOLUTIONAL NEURAL NETWORKS FOR VISUAL SEQUENCE LEARNING Public/Granted day:2021-09-02
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