Modular Machine Learning Architecture
    163.
    发明公开

    公开(公告)号:US20230147442A1

    公开(公告)日:2023-05-11

    申请号:US17831738

    申请日:2022-06-03

    Applicant: Apple Inc.

    CPC classification number: G06N3/045

    Abstract: In an example method, a system accesses first input data and a machine learning architecture. The machine learning architecture includes a first module having a first neural network, a second module having a second neural network, and a third module having a third neural network. The system generates a first feature set representing a first portion of the first input data using the first neural network, and a second feature set representing a second portion of the first input data using the second neural network. The system generates, using the third neural network, first output data based on the first feature set and the second feature set.

    Sphere projected motion estimation/compensation and mode decision

    公开(公告)号:US10999602B2

    公开(公告)日:2021-05-04

    申请号:US15390202

    申请日:2016-12-23

    Applicant: Apple Inc.

    Abstract: Techniques are disclosed for coding video data predictively based on predictions made from spherical-domain projections of input pictures to be coded and reference pictures that are prediction candidates. Spherical projection of an input picture and the candidate reference pictures may be generated. Thereafter, a search may be conducted for a match between the spherical-domain representation of a pixel block to be coded and a spherical-domain representation of the reference picture. On a match, an offset may be determined between the spherical-domain representation of the pixel block to a matching portion of the of the reference picture in the spherical-domain representation. The spherical-domain offset may be transformed to a motion vector in a source-domain representation of the input picture, and the pixel block may be coded predictively with reference to a source-domain representation of the matching portion of the reference picture.

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