公开(公告)号：US20240160937A1

公开(公告)日：2024-05-16

申请号：US18418197

申请日：2024-01-19

Applicant: Google LLC

Inventor： Rui Zhang ,  Jia Li ,  Tomas Jon Pfister

IPC: G06N3/084 ,  G06F18/21 ,  G06F18/2134 ,  G06F18/214 ,  G06F18/22 ,  G06F18/2413 ,  G06N3/045 ,  G06N3/047 ,  G06V10/74 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82

CPC classification number: G06N3/084 ,  G06F18/21347 ,  G06F18/214 ,  G06F18/2148 ,  G06F18/2193 ,  G06F18/22 ,  G06F18/2413 ,  G06N3/045 ,  G06N3/047 ,  G06V10/761 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82

Abstract: A method includes obtaining a source training dataset that includes a plurality of source training images and obtaining a target training dataset that includes a plurality of target training images. For each source training image, the method includes translating, using the forward generator neural network G, the source training image to a respective translated target image according to current values of forward generator parameters. For each target training image, the method includes translating, using a backward generator neural network F, the target training image to a respective translated source image according to current values of backward generator parameters. The method also includes training the forward generator neural network G jointly with the backward generator neural network F by adjusting the current values of the forward generator parameters and the backward generator parameters to optimize an objective function.

公开(公告)号：US20240153297A1

公开(公告)日：2024-05-09

申请号：US18501982

申请日：2023-11-03

Applicant: Google LLC

Inventor： Zizhao Zhang ,  Zifeng Wang ,  Vincent Perot ,  Jacob Devlin ,  Chen-Yu Lee ,  Guolong Su ,  Hao Zhang ,  Tomas Jon Pfister

IPC: G06V30/24 ,  G06F16/21 ,  G06V30/19 ,  G06V30/412

CPC classification number: G06V30/24 ,  G06F16/211 ,  G06V30/19147 ,  G06V30/412

Abstract: A method for extracting entities comprises obtaining a document that includes a series of textual fields that includes a plurality of entities. Each entity represents information associated with a predefined category. The method includes generating, using the document, a series of tokens representing the series of textual fields. The method includes generating an entity prompt that includes the series of tokens and one of the plurality of entities and generating a schema prompt that includes a schema associated with the document. The method includes generating a model query that includes the entity prompt and the schema prompt and determining, using an entity extraction model and the model query, a location of the one of the plurality of entities among the series of tokens. The method includes extracting, from the document, the one of the plurality of entities using the location of the one of the plurality of entities.

公开(公告)号：US11941531B1

公开(公告)日：2024-03-26

申请号：US16785032

申请日：2020-02-07

Applicant: Google LLC

Inventor： Sercan Omer Arik ,  Tomas Jon Pfister

IPC: G06N3/088 ,  G06F40/30 ,  G06N3/045

CPC classification number: G06N3/088 ,  G06F40/30 ,  G06N3/045

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing an input data element to generate a prediction output that characterizes the input data element. In one aspect, a method comprises: determining a respective attention weight between an input data element and each of a plurality of reference data elements; processing each of the reference data elements using the encoder neural network to generate a respective value embedding of each reference data element; determining a combined value embedding of the reference data elements based on (i) the respective value embedding of each reference data element, and (ii) the respective attention weight between the input data element and each reference data element; and processing the combined value embedding of the reference data elements using a prediction neural network to generate the prediction output that characterizes the input data element.

公开(公告)号：US11941084B2

公开(公告)日：2024-03-26

申请号：US17454605

申请日：2021-11-11

Applicant: Google LLC

Inventor： Kihyuk Sohn ,  Chun-Liang Li ,  Jinsung Yoon ,  Tomas Jon Pfister

IPC: G06F18/214 ,  G06N3/08 ,  G06V10/22 ,  G06V10/774 ,  G06V10/82

CPC classification number: G06F18/2155 ,  G06N3/08 ,  G06V10/22

Abstract: A method for training a machine learning model includes obtaining a set of training samples. For each training sample in the set of training samples, during each of one or more training iterations, the method includes cropping the training sample to generate a first cropped image, cropping the training sample to generate a second cropped image that is different than the first cropped image, and duplicating a first portion of the second cropped image. The method also includes overlaying the duplicated first portion of the second cropped image on a second portion of the second cropped image to form an augmented second cropped image. The first portion is different than the second portion. The method also includes training the machine learning model with the first cropped image and the augmented second cropped image.

公开(公告)号：US20230351192A1

公开(公告)日：2023-11-02

申请号：US18348587

申请日：2023-07-07

Applicant: Google LLC

Inventor： Zizhao Zhang ,  Sercan Omer Arik ,  Tomas Jon Pfister ,  Han Zhang

IPC: G06N3/084 ,  G06N20/00 ,  G06N5/04 ,  G06V10/762 ,  G06V10/771 ,  G06V10/774 ,  G06V10/776 ,  G06V10/82

CPC classification number: G06N3/084 ,  G06N20/00 ,  G06N5/04 ,  G06V10/763 ,  G06V10/771 ,  G06V10/774 ,  G06V10/776 ,  G06V10/82

Abstract: A method for training a model comprises obtaining a set of labeled training samples each associated with a given label. For each labeled training sample, the method includes generating a pseudo label and estimating a weight of the labeled training sample indicative of an accuracy of the given label. The method also includes determining whether the weight of the labeled training sample satisfies a weight threshold. When the weight of the labeled training sample satisfies the weight threshold, the method includes adding the labeled training sample to a set of cleanly labeled training samples. Otherwise, the method includes adding the labeled training sample to a set of mislabeled training samples. The method includes training the model with the set of cleanly labeled training samples using corresponding given labels and the set of mislabeled training samples using corresponding pseudo labels.

公开(公告)号：US20230325676A1

公开(公告)日：2023-10-12

申请号：US18333998

申请日：2023-06-13

Applicant: Google LLC

Inventor： Zizhao Zhang ,  Tomas Jon Pfister ,  Sercan Omer Arik ,  Mingfei Gao

IPC: G06N3/084 ,  G06N20/00 ,  G06F7/24 ,  G06N3/08 ,  G06F18/211 ,  G06F18/214

CPC classification number: G06N3/084 ,  G06N20/00 ,  G06F18/2155 ,  G06N3/08 ,  G06F18/211 ,  G06F7/24

Abstract: A method includes obtaining a set of unlabeled training samples. For each training sample in the set of unlabeled training samples generating, the method includes using a machine learning model and the training sample, a corresponding first prediction, generating, using the machine learning model and a modified unlabeled training sample, a second prediction, the modified unlabeled training sample based on the training sample, and determining a difference between the first prediction and the second prediction. The method includes selecting, based on the differences, a subset of the set of unlabeled training samples. For each training sample in the subset of the set of unlabeled training samples, the method includes obtaining a ground truth label for the training sample, and generating a corresponding labeled training sample based on the training sample paired with the ground truth label. The method includes training the machine learning model using the corresponding labeled training samples.

公开(公告)号：US11487970B2

公开(公告)日：2022-11-01

申请号：US17031144

申请日：2020-09-24

Applicant: Google LLC

Inventor： Sercan Omer Arik ,  Chen Xing ,  Zizhao Zhang ,  Tomas Jon Pfister

IPC: G06K9/62 ,  G06N3/04 ,  G06N3/08

Abstract: A method for jointly training a classification model and a confidence model. The method includes receiving a training data set including a plurality of training data subsets. From two or more training data subsets in the training data set, the method includes selecting a support set of training examples and a query set of training examples. The method includes determining, using the classification model, a centroid value for each respective class. For each training example in the query set of training examples, the method includes generating, using the classification model, a query encoding, determining a class distance measure, determining a ground-truth distance, and updating parameters of the classification model. For each training example in the query set of training examples identified as being misclassified, the method further includes generating a standard deviation value, sampling a new query, and updating parameters of the confidence model based on the new query encoding.

公开(公告)号：US20220156521A1

公开(公告)日：2022-05-19

申请号：US17454605

申请日：2021-11-11

Applicant: Google LLC

Inventor： Kihyuk Sohn ,  Chun-Liang Li ,  Jinsung Yoon ,  Tomas Jon Pfister

IPC: G06K9/62 ,  G06K9/20 ,  G06N3/08

Abstract: A method for training a machine learning model includes obtaining a set of training samples. For each training sample in the set of training samples, during each of one or more training iterations, the method includes cropping the training sample to generate a first cropped image, cropping the training sample to generate a second cropped image that is different than the first cropped image, and duplicating a first portion of the second cropped image. The method also includes overlaying the duplicated first portion of the second cropped image on a second portion of the second cropped image to form an augmented second cropped image. The first portion is different than the second portion. The method also includes training the machine learning model with the first cropped image and the augmented second cropped image.