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    METHOD AND SYSTEM FOR PERFORMING EYE TRACKING USING AN OFF-AXIS CAMERA
    2.
    发明申请
    METHOD AND SYSTEM FOR PERFORMING EYE TRACKING USING AN OFF-AXIS CAMERA 有权

    公开(公告)号：US20210182554A1

    公开(公告)日：2021-06-17

    申请号：US17129669

    申请日：2020-12-21

    Applicant: Magic Leap, Inc.

    Inventor： Vijay Badrinarayanan ,  Zhengyang Wu ,  Srivignesh Rajendran ,  Andrew Rabinovich

    IPC: G06K9/00 ,  G06T7/11 ,  G06T7/00 ,  G06N3/08

    Abstract: Systems and methods for estimating a gaze vector of an eye using a trained neural network. An input image of the eye may be received from a camera. The input image may be provided to the neural network. Network output data may be generated using the neural network. The network output data may include two-dimensional (2D) pupil data, eye segmentation data, and/or cornea center data. The gaze vector may be computed based on the network output data. The neural network may be previously trained by providing a training input image to the neural network, generating training network output data, receiving ground-truth (GT) data, computing error data based on a difference between the training network output data and the GT data, and modifying the neural network based on the error data.

    Meta-learning for multi-task learning for neural networks
    4.
    发明授权
    Meta-learning for multi-task learning for neural networks 有权

    公开(公告)号：US11853894B2

    公开(公告)日：2023-12-26

    申请号：US17344758

    申请日：2021-06-10

    Applicant: Magic Leap, Inc.

    Inventor： Andrew Rabinovich ,  Vijay Badrinarayanan ,  Srivignesh Rajendran ,  Chen-Yu Lee

    IPC: G06N3/084 ,  G06F18/21 ,  G06N3/04 ,  G06N3/044 ,  G06N3/047

    CPC classification number: G06N3/084 ,  G06F18/217 ,  G06N3/04 ,  G06N3/044 ,  G06N3/047

    Abstract: Methods and systems for meta-learning are described for automating learning of child tasks with a single neural network. The order in which tasks are learned by the neural network can affect performance of the network, and the meta-learning approach can use a task-level curriculum for multi-task training. The task-level curriculum can be learned by monitoring a trajectory of loss functions during training. The meta-learning approach can learn to adapt task loss balancing weights in the course of training to get improved performance on multiple tasks on real world datasets. Advantageously, learning to dynamically balance weights among different task losses can lead to superior performance over the use of static weights determined by expensive random searches or heuristics. Embodiments of the meta-learning approach can be used for computer vision tasks or natural language processing tasks, and the trained neural networks can be used by augmented or virtual reality devices.

    META-LEARNING FOR MULTI-TASK LEARNING FOR NEURAL NETWORKS
    10.
    发明申请
    META-LEARNING FOR MULTI-TASK LEARNING FOR NEURAL NETWORKS 有权

    公开(公告)号：US20210406609A1

    公开(公告)日：2021-12-30

    申请号：US17344758

    申请日：2021-06-10

    Applicant: Magic Leap, Inc.

    Inventor： Andrew Rabinovich ,  Vijay Badrinarayanan ,  Srivignesh Rajendran ,  Chen-Yu Lee

    IPC: G06K9/62 ,  G06N3/04 ,  G06N3/08

    Abstract: Methods and systems for meta-learning are described for automating learning of child tasks with a single neural network. The order in which tasks are learned by the neural network can affect performance of the network, and the meta-learning approach can use a task-level curriculum for multi-task training. The task-level curriculum can be learned by monitoring a trajectory of loss functions during training. The meta-learning approach can learn to adapt task loss balancing weights in the course of training to get improved performance on multiple tasks on real world datasets. Advantageously, learning to dynamically balance weights among different task losses can lead to superior performance over the use of static weights determined by expensive random searches or heuristics. Embodiments of the meta-learning approach can be used for computer vision tasks or natural language processing tasks, and the trained neural networks can be used by augmented or virtual reality devices.

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