公开(公告)号：US20200057936A1

公开(公告)日：2020-02-20

申请号：US16461287

申请日：2017-11-15

Applicant: GOOGLE LLC

Inventor： Philip Haeusser ,  Alexander Mordvintsev

IPC: G06N3/04 ,  G06N3/08 ,  G06F17/18 ,  G06F17/16 ,  G06K9/62

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network. One of the methods includes obtaining a batch of labeled training items and a batch of unlabeled training items; processing the labeled training items and the unlabeled training items using the neural network and in accordance with current values of the network parameters to generate respective embeddings; determining a plurality of similarity values, each similarity value measuring a similarity between the embedding for a respective labeled training item and the embedding for a respective unlabeled training item; determining a respective roundtrip path probability for each of a plurality of roundtrip paths; and performing an iteration of a neural network training procedure to determine a first value update to the current values of the network parameters that decreases roundtrip path probabilities for incorrect roundtrip paths.

公开(公告)号：US11443170B2

公开(公告)日：2022-09-13

申请号：US16461287

申请日：2017-11-15

Applicant: GOOGLE LLC

Inventor： Philip Haeusser ,  Alexander Mordvintsev

IPC: G06N3/04 ,  G06F17/16 ,  G06F17/18 ,  G06K9/62 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network. One of the methods includes obtaining a batch of labeled training items and a batch of unlabeled training items; processing the labeled training items and the unlabeled training items using the neural network and in accordance with current values of the network parameters to generate respective embeddings; determining a plurality of similarity values, each similarity value measuring a similarity between the embedding for a respective labeled training item and the embedding for a respective unlabeled training item; determining a respective roundtrip path probability for each of a plurality of roundtrip paths; and performing an iteration of a neural network training procedure to determine a first value update to the current values of the network parameters that decreases roundtrip path probabilities for incorrect roundtrip paths.