公开(公告)号：US11544498B2

公开(公告)日：2023-01-03

申请号：US17194090

申请日：2021-03-05

Applicant: Google LLC

Inventor： Ariel Gordon ,  Soeren Pirk ,  Anelia Angelova ,  Vincent Michael Casser ,  Yao Lu ,  Anthony Brohan ,  Zhao Chen ,  Jan Dlabal

IPC: G06K9/62 ,  G06N3/04 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network using consistency measures. One of the methods includes processing a particular training example from a mediator training data set using a first neural network to generate a first output for a first machine learning task; processing the particular training example in the mediator training data set using each of one or more second neural networks, wherein each second neural network is configured to generate a second output for a respective second machine learning task; determining, for each second machine learning task, a consistency target output for the first machine learning task; determining, for each second machine learning task, an error between the first output and the consistency target output corresponding to the second machine learning task; and generating a parameter update for the first neural network from the determined errors.

公开(公告)号：US20210279511A1

公开(公告)日：2021-09-09

申请号：US17194090

申请日：2021-03-05

Applicant: Google LLC

Inventor： Ariel Gordon ,  Soeren Pirk ,  Anelia Angelova ,  Vincent Michael Casser ,  Yao Lu ,  Anthony Brohan ,  Zhao Chen ,  Jan Dlabal

IPC: G06K9/62 ,  G06N3/08 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network using consistency measures. One of the methods includes processing a particular training example from a mediator training data set using a first neural network to generate a first output for a first machine learning task; processing the particular training example in the mediator training data set using each of one or more second neural networks, wherein each second neural network is configured to generate a second output for a respective second machine learning task; determining, for each second machine learning task, a consistency target output for the first machine learning task; determining, for each second machine learning task, an error between the first output and the consistency target output corresponding to the second machine learning task; and generating a parameter update for the first neural network from the determined errors.