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    Inspection neural network for assessing neural network reliability
    2.
    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.

    Encoding three-dimensional data for processing by capsule neural networks
    3.
    发明授权
    Encoding three-dimensional data for processing by capsule neural networks 有权

    公开(公告)号：US12008790B2

    公开(公告)日：2024-06-11

    申请号：US16836028

    申请日：2020-03-31

    Applicant: Apple Inc.

    Inventor： Nitish Srivastava ,  Ruslan Salakhutdinov ,  Hanlin Goh

    IPC: G06T9/00 ,  G06N3/047 ,  G06N3/08 ,  H04N13/111 ,  H04N13/161

    CPC classification number: G06T9/002 ,  G06N3/047 ,  G06N3/08 ,  H04N13/111 ,  H04N13/161

    Abstract: A method includes defining a geometric capsule that is interpretable by a capsule neural network, wherein the geometric capsule includes a feature representation and a pose. The method also includes determining multiple viewpoints relative to the geometric capsule and determining a first appearance representation of the geometric capsule for each of the multiple viewpoints. The method also includes determining a transform for each of the multiple viewpoints that moves each of the multiple viewpoints to a respective transformed viewpoint and determining second appearance representations that each correspond to one of the transformed viewpoints. The method also includes combining the second appearance representations to define an agreed appearance representation. The method also includes updating the feature representation for the geometric capsule based on the agreed appearance representation.

    Encoding Three-Dimensional Data For Processing By Capsule Neural Networks
    4.
    发明申请
    Encoding Three-Dimensional Data For Processing By Capsule Neural Networks 有权

    公开(公告)号：US20210090302A1

    公开(公告)日：2021-03-25

    申请号：US16836028

    申请日：2020-03-31

    Applicant: Apple Inc.

    Inventor： Nitish Srivastava ,  Ruslan Salakhutdinov ,  Hanlin Goh

    IPC: G06T9/00 ,  H04N13/111 ,  H04N13/161 ,  G06N3/04 ,  G06N3/08

    Abstract: A method includes defining a geometric capsule that is interpretable by a capsule neural network, wherein the geometric capsule includes a feature representation and a pose. The method also includes determining multiple viewpoints relative to the geometric capsule and determining a first appearance representation of the geometric capsule for each of the multiple viewpoints. The method also includes determining a transform for each of the multiple viewpoints that moves each of the multiple viewpoints to a respective transformed viewpoint and determining second appearance representations that each correspond to one of the transformed viewpoints. The method also includes combining the second appearance representations to define an agreed appearance representation. The method also includes updating the feature representation for the geometric capsule based on the agreed appearance representation.

    Encoding Three-Dimensional Data For Processing By Capsule Neural Networks
    6.
    发明公开
    Encoding Three-Dimensional Data For Processing By Capsule Neural Networks 审中-公开

    公开(公告)号：US20240331207A1

    公开(公告)日：2024-10-03

    申请号：US18738488

    申请日：2024-06-10

    Applicant: APPLE INC.

    Inventor： Nitish Srivastava ,  Ruslan Salakhutdinov ,  Hanlin Goh

    IPC: G06T9/00 ,  G06N3/047 ,  G06N3/08 ,  H04N13/111 ,  H04N13/161

    CPC classification number: G06T9/002 ,  G06N3/047 ,  G06N3/08 ,  H04N13/111 ,  H04N13/161

    Abstract: A method includes receiving three-dimensional geometric elements as an input. The method also includes initializing geometric capsules by assigning one of the three-dimensional geometric elements to each of the geometric capsules and setting initial values for a pose component and a feature component of each of the geometric capsules. The method also includes one or more iterations of a routing procedure that includes assigning an additional one of the three-dimensional geometric elements to a respective one of the geometric capsules, based on correspondence of the additional one of the three-dimensional geometric elements to a surface defined based on the feature component of the respective one of the geometric capsules, and updating the feature component of each of the geometric capsules based on the three-dimensional geometric elements assigned to each of the geometric capsules. The method also includes outputting the geometric capsules including encoded three-dimensional data.

    Generative scene networks
    8.
    发明授权
    Generative scene networks 有权

    公开(公告)号：US12198275B2

    公开(公告)日：2025-01-14

    申请号：US17689851

    申请日：2022-03-08

    Applicant: Apple Inc.

    Inventor： Miguel Angel Bautista Martin ,  Nitish Srivastava ,  Joshua M. Susskind ,  Terrance Devries

    IPC: G06T19/00 ,  G06T15/55 ,  G06T19/20

    Abstract: Implementations of the subject technology relate to generative scene networks (GSNs) that are able to generate realistic scenes that can be rendered from a free moving camera at any location and orientation. A GSN may be implemented using a global generator and a locally conditioned radiance field. GSNs may employ a spatial latent representation as conditioning for a grid of locally conditioned radiance fields, and may be trained using an adversarial learning framework. Inverting a GSN may allow free navigation of a generated scene conditioned on one or more observations.

    PARTIALLY SHARED NEURAL NETWORKS FOR MULTIPLE TASKS
    10.
    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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