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公开(公告)号:US10748039B2
公开(公告)日:2020-08-18
申请号:US16586262
申请日:2019-09-27
Applicant: DeepMind Technologies Limited
Inventor: Catalin-Dumitru Ionescu , Tejas Dattatraya Kulkarni
Abstract: A reinforcement learning neural network system in which internal representations and policies are grounded in visual entities derived from image pixels comprises a visual entity identifying neural network subsystem configured to process image data to determine a set of spatial maps representing respective discrete visual entities. A reinforcement learning neural network subsystem processes data from the set of spatial maps and environmental reward data to provide action data for selecting actions to perform a task.
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公开(公告)号:US20220215580A1
公开(公告)日:2022-07-07
申请号:US17608620
申请日:2020-05-05
Applicant: DeepMind Technologies Limited
Inventor: Ankush Gupta , Tejas Dattatraya Kulkarni
IPC: G06T7/73
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for unsupervised learning of object keypoint locations in images. In particular, a keypoint extraction machine learning model having a plurality of keypoint model parameters is trained to receive an input image and to process the input image in accordance with the keypoint model parameters to generate a plurality of keypoint locations in the input image. The machine learning model is trained using either temporal transport or spatio-temporal transport.
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公开(公告)号:US20200104645A1
公开(公告)日:2020-04-02
申请号:US16586262
申请日:2019-09-27
Applicant: DeepMind Technologies Limited
Inventor: Catalin-Dumitru Ionescu , Tejas Dattatraya Kulkarni
Abstract: A reinforcement learning neural network system in which internal representations and policies are grounded in visual entities derived from image pixels comprises a visual entity identifying neural network subsystem configured to process image data to determine a set of spatial maps representing respective discrete visual entities. A reinforcement learning neural network subsystem processes data from the set of spatial maps and environmental reward data to provide action data for selecting actions to perform a task.
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公开(公告)号:US12175723B2
公开(公告)日:2024-12-24
申请号:US17608620
申请日:2020-05-05
Applicant: DeepMind Technologies Limited
Inventor: Ankush Gupta , Tejas Dattatraya Kulkarni
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for unsupervised learning of object keypoint locations in images. In particular, a keypoint extraction machine learning model having a plurality of keypoint model parameters is trained to receive an input image and to process the input image in accordance with the keypoint model parameters to generate a plurality of keypoint locations in the input image. The machine learning model is trained using either temporal transport or spatio-temporal transport.
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公开(公告)号:US20210271968A1
公开(公告)日:2021-09-02
申请号:US16967597
申请日:2019-02-11
Applicant: DeepMind Technologies Limited
Inventor: Iaroslav Ganin , Tejas Dattatraya Kulkarni , Oriol Vinyals , Seyed Mohammadali Eslami
Abstract: A generative adversarial neural network system to provide a sequence of actions for performing a task. The system comprises a reinforcement learning neural network subsystem coupled to a simulator and a discriminator neural network. The reinforcement learning neural network subsystem includes a policy recurrent neural network to, at each of a sequence of time steps, select one or more actions to be performed according to an action selection policy, each action comprising one or more control commands for a simulator. The simulator is configured to implement the control commands for the time steps to generate a simulator output. The discriminator neural network is configured to discriminate between the simulator output and training data, to provide a reward signal for the reinforcement learning. The simulator may be non-differentiable simulator, for example a computer program to produce an image or audio waveform or a program to control a robot or vehicle.
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