公开(公告)号：US10586111B2

公开(公告)日：2020-03-10

申请号：US15444054

申请日：2017-02-27

Applicant: Google Inc.

Inventor： Filip Pavetic ,  King Hong Thomas Leung ,  Dmitrii Tochilkin

IPC: G06K9/62 ,  G06K9/00 ,  G06K9/32 ,  G06N20/00

Abstract: A system and methods are disclosed for training a machine learning model to identify constituent images within composite images. In one implementation, a composite image is generated, where the composite image comprises a first portion containing pixel data of a first constituent image, and a second portion containing pixel data of a second constituent image. A first training input comprising pixel data of the composite image and a first target output for the first training input are generated, where the first target output identifies a position of the first portion within the composite image. The training data is provided to train the machine learning model on (i) a set of training inputs comprising the first training input and (ii) a set of target outputs comprising the first target output.

公开(公告)号：US20180204065A1

公开(公告)日：2018-07-19

申请号：US15444054

申请日：2017-02-27

Applicant: Google Inc.

Inventor： Filip Pavetic ,  King Hong Thomas Leung ,  Dmitrii Tochilkin

IPC: G06K9/00 ,  G06K9/62

CPC classification number: G06K9/00718 ,  G06K9/00711 ,  G06K9/3233 ,  G06K9/6256 ,  G06K9/6268 ,  G06N20/00

Abstract: A system and methods are disclosed for training a machine learning model to identify constituent images within composite images. In one implementation, a composite image is generated, where the composite image comprises a first portion containing pixel data of a first constituent image, and a second portion containing pixel data of a second constituent image. A first training input comprising pixel data of the composite image and a first target output for the first training input are generated, where the first target output identifies a position of the first portion within the composite image. The training data is provided to train the machine learning model on (i) a set of training inputs comprising the first training input and (ii) a set of target outputs comprising the first target output.