Invention Grant
- Patent Title: Reducing computational costs of deep reinforcement learning by gated convolutional neural network
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Application No.: US16039679Application Date: 2018-07-19
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Publication No.: US10671891B2Publication Date: 2020-06-02
- Inventor: Shohei Ohsawa , Takayuki Osogami
- Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION
- Applicant Address: US NY Armonk
- Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
- Current Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
- Current Assignee Address: US NY Armonk
- Agency: Tutunjian & Bitteto, P.C.
- Agent Vazken Alexanian
- Main IPC: G06K9/00
- IPC: G06K9/00 ; G06K9/62 ; G06N3/08 ; G06N3/04

Abstract:
A method is provided for reducing a computational cost of deep reinforcement learning using an input image to provide a filtered output image composed of pixels. The method includes generating a moving gate in which the pixels of the filtered output image to be masked are assigned a first gate value and the pixels of the filtered output image to be passed through are assigned a second gate value. The method further includes applying the input image and the moving gate to a GCNN to provide the filtered output image such that only the pixels of the input image used to compute the pixels assigned the second gate value are processed by the GCNN while bypassing the pixels of the input image useable to compute the pixels assigned the first gate to reduce an overall processing time of the input image in order to provide the filtered output image.
Public/Granted literature
- US20200026963A1 REDUCING COMPUTATIONAL COSTS OF DEEP REINFORCEMENT LEARNING BY GATED CONVOLUTIONAL NEURAL NETWORK Public/Granted day:2020-01-23
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