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    SOURCE-FREE ACTIVE ADAPTATION TO DISTRIBUTIONAL SHIFTS FOR MACHINE LEARNING
    1.
    发明申请
    SOURCE-FREE ACTIVE ADAPTATION TO DISTRIBUTIONAL SHIFTS FOR MACHINE LEARNING 有权

    公开(公告)号：US20230137905A1

    公开(公告)日：2023-05-04

    申请号：US18089513

    申请日：2022-12-27

    Applicant: Intel Corporation

    Inventor： Amrutha Machireddy ,  Ranganath Krishnan ,  Nilesh Ahuja ,  Omesh Tickoo

    IPC: G06N3/091

    Abstract: Disclosed is an example solution to perform source-free active adaptation to distributional shifts for machine learning. The example solution includes: interface circuitry; programmable circuitry; and instructions to cause the programmable circuitry to: perform a first training of a neural network on a baseline data set associated with a first data distribution; compare data of a shifted data set to a threshold uncertainty value, wherein the threshold uncertainty value is associated with a distributional shift between the baseline data set and the shifted data set; generate a shifted data subset including items of the shifted dataset that satisfy the threshold uncertainty value; and perform a second training of the neural network based on the shifted data subset.

    ENSEMBLE LEARNING FOR DEEP FEATURE DEFECT DETECTION
    2.
    发明申请
    ENSEMBLE LEARNING FOR DEEP FEATURE DEFECT DETECTION 有权

    公开(公告)号：US20220004935A1

    公开(公告)日：2022-01-06

    申请号：US17481553

    申请日：2021-09-22

    Applicant: Intel Corporation

    Inventor： Barath Lakshmanan ,  Craig Sperry ,  David Austin ,  Nilesh Ahuja

    IPC: G06N20/20 ,  G06N7/00 ,  G06N3/04

    Abstract: An apparatus to facilitate ensemble learning for deep feature defect detection is disclosed. The apparatus includes one or more processors to receive a deep feature vector from a feature extractor of an ensemble learning system, the deep feature vector extracted from input data; cluster the deep feature vector into a plurality of clusters based on a distance into the plurality of clusters; execute a probabilistic machine learning model corresponding to a cluster of the plurality of clusters to which the deep feature vector is clustered; and detect whether the deep feature vector comprises a defect based on an output of execution of the probabilistic machine learning model.

    DETECTION OF AN ANOMALOUS IMAGE ASSOCIATED WITH IMAGE DATA FROM ONE OR MORE CAMERAS OF A COMPUTER-AIDED OR AUTONOMOUS DRIVING VEHICLE
    7.
    发明申请
    DETECTION OF AN ANOMALOUS IMAGE ASSOCIATED WITH IMAGE DATA FROM ONE OR MORE CAMERAS OF A COMPUTER-AIDED OR AUTONOMOUS DRIVING VEHICLE 审中-公开

    公开(公告)号：US20190225234A1

    公开(公告)日：2019-07-25

    申请号：US16369898

    申请日：2019-03-29

    Applicant: Intel Corporation

    Inventor： Avinash Kumar ,  Sridhar Sharma ,  Nilesh Ahuja

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

    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.

    DETECTION OF ANOMALIES IN THREE-DIMENSIONAL IMAGES
    8.
    发明申请
    DETECTION OF ANOMALIES IN THREE-DIMENSIONAL IMAGES 有权

    公开(公告)号：US20240412366A1

    公开(公告)日：2024-12-12

    申请号：US18812700

    申请日：2024-08-22

    Applicant: Intel Corporation

    Inventor： Jiaxiang Jiang ,  Athmanarayanan Lakshmi Narayanan ,  Nilesh Ahuja ,  Ibrahima Jacques Ndiour ,  Ergin Utku Genc ,  Mahesh Subedar ,  Omesh Tickoo

    IPC: G06T7/00

    Abstract: Systems, apparatus, articles of manufacture, and methods to detect anomalies in three-dimensional (3D) images are disclosed. Example apparatus disclosed herein generate a first two-dimensional (2D) anomaly map corresponding to a first 2D image slice of a 3D image, the first 2D image slice corresponding to a first axis of the 3D image. Disclosed example apparatus also generate a second 2D anomaly map corresponding to a second 2D image slice of the 3D image, the second 2D image slice corresponding to a second axis of the 3D image. Disclosed example apparatus further generate a 3D anomaly volume based on the first 2D anomaly map and the second 2D anomaly detection, the 3D anomaly volume corresponding to the 3D image.

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