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    OUT-OF-DISTRIBUTION DETECTION USING A NEURAL NETWORK
    14.
    发明公开
    OUT-OF-DISTRIBUTION DETECTION USING A NEURAL NETWORK 审中-公开

    公开(公告)号：US20230298322A1

    公开(公告)日：2023-09-21

    申请号：US18325436

    申请日：2023-05-30

    Applicant: Intel Corporation

    Inventor： Ibrahima Ndiour ,  Nilesh Ahuja ,  Ranganath Krishnan ,  Mahesh Subedar ,  Omesh Tickoo ,  Ergin Genc

    IPC: G06V10/77 ,  G06V10/82 ,  G06V10/80

    CPC classification number: G06V10/7715 ,  G06V10/82 ,  G06V10/80

    Abstract: Features extracted from one or more layers of a trained deep neural network (DNN) are used to detect out-of-distribution (OOD) data, such as anomalies. An OOD detection process includes transforming a feature output from a layer of the DNN from a relatively high-dimensional feature space to a lower-dimensional space, and then performing a reverse transformation back to the higher-dimensional feature space, resulting in a reconstructed feature. A feature reconstruction error is calculated based on a difference between the reconstructed feature and the original feature output from the DNN. The OOD detection process may further include calculating a score based on the feature reconstruction error and generating a visual representation of the feature reconstruction error.

    Safety system for a vehicle
    15.
    发明授权
    Safety system for a vehicle 有权

    公开(公告)号：US11314258B2

    公开(公告)日：2022-04-26

    申请号：US16727955

    申请日：2019-12-27

    Applicant: Intel Corporation

    Inventor： David Gomez Gutierrez ,  Ranganath Krishnan ,  Javier Felip Leon ,  Nilesh Ahuja ,  Ibrahima Ndiour

    IPC: G05D1/02 ,  B60W50/02 ,  B60W30/095 ,  B60W50/00 ,  B60W40/02

    Abstract: A safety system for a vehicle may include one or more processors configured to determine uncertainty data indicating uncertainty in one or more predictions from a driving model during operation of a vehicle; change or update one or more of the driving model parameters to one or more changed or updated driving model parameters based on the determined uncertainty data; and provide the one or more changed or updated driving model parameters to a control system of the vehicle for controlling the vehicle to operate in accordance with the driving model including the one or more changed or updated driving model parameters.

    Detection of an anomalous image associated with image data from one or more cameras of a computer-aided or autonomous driving vehicle
    16.
    发明授权
    Detection of an anomalous image associated with image data from one or more cameras of a computer-aided or autonomous driving vehicle 有权

    公开(公告)号：US11148676B2

    公开(公告)日：2021-10-19

    申请号：US16369898

    申请日：2019-03-29

    Applicant: Intel Corporation

    Inventor： Avinash Kumar ,  Sridhar Sharma ,  Nilesh Ahuja

    IPC: B60W50/02 ,  G06T7/00 ,  B60R11/04 ,  G06K9/00 ,  G06T7/80

    Abstract: Embodiments include apparatuses, systems, and methods for a computer-aided or autonomous driving (CA/AD) system to detect an anomalous image associated with image data from one or more cameras of a computer-aided or autonomous driving (CA/AD) vehicle. Embodiments may include a sensor interface disposed in the CA/AD vehicle to receive, from the one or more cameras, a stream of image data including single view image data captured by the one or more cameras or multi-view image data collaboratively captured by multiple ones of the one or more cameras. In embodiments, a consistency analysis unit disposed in the CA/AD vehicle is coupled to the sensor interface to perform a consistency check on pixel-level data using single view or multi-view geometric methods to determine whether the image data includes an anomalous image. Other embodiments may also be described and claimed.

    METHODS AND APPARATUS TO FACILITATE CONTINUOUS LEARNING
    17.
    发明申请
    METHODS AND APPARATUS TO FACILITATE CONTINUOUS LEARNING 有权

    公开(公告)号：US20210117792A1

    公开(公告)日：2021-04-22

    申请号：US17132858

    申请日：2020-12-23

    Applicant: Intel Corporation

    Inventor： Nilesh Ahuja ,  Mahesh Subedar ,  Ranganath Krishnan ,  Ibrahima Ndiour ,  Omesh Tickoo

    IPC: G06N3/08 ,  G06N3/04 ,  G06F7/58

    Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to facilitate continuous learning. An example apparatus includes a trainer to train a first Bayesian neural network (BNN) and a second BNN, the first BNN associated with a first weight distribution and the second BNN associated with a second weight distribution. The example apparatus includes a weight determiner to determine a first sampling weight associated with the first BNN and a second sampling weight associated with the second BNN. The example apparatus includes a network sampler to sample at least one of the first weight distribution or the second weight distribution based on a pseudo-random number, the first sampling weight, and the second sampling weight. The example apparatus includes an inference controller to generate an ensemble weight distribution based on the sample.

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