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    Inspection neural network for assessing neural network reliability
    1.
    发明授权
    Inspection neural network for assessing neural network reliability 有权

    公开(公告)号：US10943148B2

    公开(公告)日：2021-03-09

    申请号：US15828408

    申请日：2017-11-30

    Applicant: Apple Inc.

    Inventor： Rui Hu ,  Ruslan Salakhutdinov ,  Nitish Srivastava ,  YiChuan Tang

    IPC: G06K9/62 ,  G06N3/04 ,  G06N3/08 ,  G05D1/02 ,  G06K9/00 ,  G06K9/46 ,  G06K9/66

    Abstract: A system employs an inspection neural network (INN) to inspect data generated during an inference process of a primary neural network (PNN) to generate an indication of reliability for an output generated by the PNN. The system includes a sensor configured to capture sensor data. Sensor data captured by the sensor is provided to a data analyzer to generate an output using the PNN. An analyzer inspector is configured to capture inspection data associated with the generation of the output by the data analyzer, and use the INN to generate an indication of reliability for the PNN's output based on the inspection data. The INN is trained using a set of training data that is distinct from the training data used to train the PNN.

    PARTIALLY SHARED NEURAL NETWORKS FOR MULTIPLE TASKS
    2.
    发明申请
    PARTIALLY SHARED NEURAL NETWORKS FOR MULTIPLE TASKS 审中-公开

    公开(公告)号：US20180157972A1

    公开(公告)日：2018-06-07

    申请号：US15828399

    申请日：2017-11-30

    Applicant: Apple Inc.

    Inventor： Rui Hu ,  Kshitiz Garg ,  Hanlin Goh ,  Ruslan Salakhutdinov ,  Nitish Srivastava ,  YiChuan Tang

    IPC: G06N3/08 ,  G06N5/04 ,  G06T1/00 ,  G06K9/00

    CPC classification number: G06N3/08 ,  G06K9/00791 ,  G06N3/0454 ,  G06N5/04 ,  G06T1/0007

    Abstract: A system includes a neural network organized into layers corresponding to stages of inferences. The neural network includes a common portion, a first portion, and a second portion. The first portion includes a first set of layers dedicated to performing a first inference task on an input data. The second portion includes a second set of layers dedicated to performing a second inference task on the same input data. The common portion includes a third set of layers, which may include an input layer to the neural network, that are used in the performance of both the first and second inference tasks. The system may receive an input data and perform both inference tasks on the input data in a single pass. During training, a training sample with annotations for both inference tasks may be used to train the neural network in a single pass.

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