公开(公告)号：US12080055B2

公开(公告)日：2024-09-03

申请号：US17697750

申请日：2022-03-17

Applicant: Google LLC

Inventor： Tsung-Yi Lin ,  Barret Zoph ,  Ekin Dogus Cubuk ,  Golnaz Ghiasi ,  Quoc V. Le

IPC: G06V10/82 ,  G06N3/084 ,  G06V10/764 ,  G06V10/77 ,  G06V10/774 ,  G06V10/776 ,  G06V10/80

CPC classification number: G06V10/82 ,  G06N3/084 ,  G06V10/764 ,  G06V10/7715 ,  G06V10/774 ,  G06V10/776 ,  G06V10/806

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training an image representation neural network.

公开(公告)号：US11847541B2

公开(公告)日：2023-12-19

申请号：US17556871

申请日：2021-12-20

Applicant: Google LLC

Inventor： Jonathon Shlens ,  Quoc V. Le ,  Ekin Dogus Cubuk ,  Barret Zoph

IPC: G06N3/08 ,  G06F18/21 ,  G06F18/214 ,  G06N20/00 ,  G06N3/04

CPC classification number: G06N20/00 ,  G06F18/214 ,  G06F18/217 ,  G06N3/08 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a machine learning model. One of the methods includes obtaining a training data set for training a machine learning model, the training data set comprising a plurality of training inputs; determining a plurality of data augmentation policies, wherein each data augmentation policy defines a procedure for processing a training input to generate a transformed training input; for each data augmentation policy, training the machine learning model using the data augmentation policy; determining, for each data augmentation policy, a quality measure of the machine learning model that has been trained using the data augmentation policy; and selecting a final data augmentation policy based using the quality measures of the machine learning models.

公开(公告)号：US20230368024A1

公开(公告)日：2023-11-16

申请号：US18226527

申请日：2023-07-26

Applicant: Google LLC

Inventor： Barret Zoph ,  Quoc V. Le

IPC: G06N3/08 ,  G06F18/21 ,  G06N3/044 ,  G06N3/045

CPC classification number: G06N3/08 ,  G06F18/217 ,  G06N3/044 ,  G06N3/045

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining neural network architectures. One of the methods includes generating, using a controller neural network, a batch of output sequences, each output sequence in the batch defining a respective architecture of a child neural network that is configured to perform a particular neural network task; for each output sequence in the batch: training a respective instance of the child neural network having the architecture defined by the output sequence; evaluating a performance of the trained instance of the child neural network on the particular neural network task to determine a performance metric for the trained instance of the child neural network on the particular neural network task; and using the performance metrics for the trained instances of the child neural network to adjust the current values of the controller parameters of the controller neural network.

公开(公告)号：US20230252327A1

公开(公告)日：2023-08-10

申请号：US18137398

申请日：2023-04-20

Applicant: Google LLC

Inventor： Vijay Vasudevan ,  Barret Zoph ,  Jonathon Shlens ,  Quoc V. Le

IPC: G06N5/046 ,  G06N3/044 ,  G06N3/045 ,  G06N3/08 ,  G06T7/00

CPC classification number: G06N5/046 ,  G06N3/08 ,  G06N3/044 ,  G06N3/045 ,  G06T7/0002 ,  G06T2207/20084

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining neural network architectures. One of the methods includes generating, using a controller neural network having controller parameters and in accordance with current values of the controller parameters, a batch of output sequences. The method includes, for each output sequence in the batch: generating an instance of a child convolutional neural network (CNN) that includes multiple instances of a first convolutional cell having an architecture defined by the output sequence; training the instance of the child CNN to perform an image processing task; and evaluating a performance of the trained instance of the child CNN on the task to determine a performance metric for the trained instance of the child CNN; and using the performance metrics for the trained instances of the child CNN to adjust current values of the controller parameters of the controller neural network.

公开(公告)号：US20210295163A1

公开(公告)日：2021-09-23

申请号：US17340959

申请日：2021-06-07

Applicant: Google LLC

Inventor： Barret Zoph ,  Quoc V. Le

IPC: G06N3/08 ,  G06N3/04 ,  G06K9/62

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining neural network architectures. One of the methods includes generating, using a controller neural network, a batch of output sequences, each output sequence in the batch defining a respective architecture of a child neural network that is configured to perform a particular neural network task; for each output sequence in the batch: training a respective instance of the child neural network having the architecture defined by the output sequence; evaluating a performance of the trained instance of the child neural network on the particular neural network task to determine a performance metric for the trained instance of the child neural network on the particular neural network task; and using the performance metrics for the trained instances of the child neural network to adjust the current values of the controller parameters of the controller neural network.

公开(公告)号：US20210019658A1

公开(公告)日：2021-01-21

申请号：US17061103

申请日：2020-10-01

Applicant: Google LLC

Inventor： Vijay Vasudevan ,  Barret Zoph ,  Ekin Dogus Cubuk ,  Quoc V. Le

IPC: G06N20/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for learning a data augmentation policy for training a machine learning model. In one aspect, a method includes: receiving training data for training a machine learning model to perform a particular machine learning task; determining multiple data augmentation policies, comprising, at each of multiple time steps: generating a current data augmentation policy based on quality measures of data augmentation policies generated at previous time steps; training a machine learning model on the training data using the current data augmentation policy; and determining a quality measure of the current data augmentation policy using the machine learning model after it has been trained using the current data augmentation policy; and selecting a final data augmentation policy based on the quality measures of the determined data augmentation policies.

公开(公告)号：US20240273410A1

公开(公告)日：2024-08-15

申请号：US18544347

申请日：2023-12-18

Applicant: Google LLC

Inventor： Jonathon Shlens ,  Quoc V. Le ,  Ekin Dogus Cubuk ,  Barret Zoph

IPC: G06N20/00 ,  G06F18/21 ,  G06F18/214 ,  G06N3/04 ,  G06N3/08

CPC classification number: G06N20/00 ,  G06F18/214 ,  G06F18/217 ,  G06N3/08 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a machine learning model. One of the methods includes obtaining a training data set for training a machine learning model, the training data set comprising a plurality of training inputs; determining a plurality of data augmentation policies, wherein each data augmentation policy defines a procedure for processing a training input to generate a transformed training input; for each data augmentation policy, training the machine learning model using the data augmentation policy; determining, for each data augmentation policy, a quality measure of the machine learning model that has been trained using the data augmentation policy; and selecting a final data augmentation policy based using the quality measures of the machine learning models.

公开(公告)号：US20230359898A1

公开(公告)日：2023-11-09

申请号：US18350464

申请日：2023-07-11

Applicant: Google LLC

Inventor： Daniel Sung-Joon Park ,  Quoc Le ,  William Chan ,  Ekin Dogus Cubuk ,  Barret Zoph ,  Yu Zhang ,  Chung-Cheng Chiu

IPC: G06V10/774 ,  G06N20/00 ,  G10L15/16 ,  G10L15/06 ,  G10L15/12 ,  G10L15/28 ,  G06V10/82

CPC classification number: G06N3/084 ,  G06N20/00 ,  G10L15/16 ,  G10L15/063 ,  G10L15/12 ,  G06V10/7747 ,  G10L15/28 ,  G06V10/82 ,  G06F18/2148

Abstract: Generally, the present disclosure is directed to systems and methods that generate augmented training data for machine-learned models via application of one or more augmentation techniques to audiographic images that visually represent audio signals. In particular, the present disclosure provides a number of novel augmentation operations which can be performed directly upon the audiographic image (e.g., as opposed to the raw audio data) to generate augmented training data that results in improved model performance. As an example, the audiographic images can be or include one or more spectrograms or filter bank sequences.

公开(公告)号：US20220301298A1

公开(公告)日：2022-09-22

申请号：US17697750

申请日：2022-03-17

Applicant: Google LLC

Inventor： Tsung-Yi Lin ,  Barret Zoph ,  Ekin Dogus Cubuk ,  Golnaz Ghiasi ,  Quoc V. Le

IPC: G06V10/82 ,  G06N3/08 ,  G06V10/774 ,  G06V10/77 ,  G06V10/776 ,  G06V10/764 ,  G06V10/80

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training an image representation neural network.

公开(公告)号：US20220114400A1

公开(公告)日：2022-04-14

申请号：US17556871

申请日：2021-12-20

Applicant: Google LLC

Inventor： Jonathon Shlens ,  Quoc V. Le ,  Ekin Dogus Cubuk ,  Barret Zoph

IPC: G06K9/62 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a machine learning model. One of the methods includes obtaining a training data set for training a machine learning model, the training data set comprising a plurality of training inputs; determining a plurality of data augmentation policies, wherein each data augmentation policy defines a procedure for processing a training input to generate a transformed training input; for each data augmentation policy, training the machine learning model using the data augmentation policy; determining, for each data augmentation policy, a quality measure of the machine learning model that has been trained using the data augmentation policy; and selecting a final data augmentation policy based using the quality measures of the machine learning models.