公开(公告)号：US11301733B2

公开(公告)日：2022-04-12

申请号：US16416848

申请日：2019-05-20

Applicant: Google LLC

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

IPC: G06K9/66 ,  G06K9/62 ,  G06T3/60 ,  G06T3/20 ,  G06T11/00 ,  G06K9/00

Abstract: Example aspects of the present disclosure are directed to systems and methods for learning data augmentation strategies for improved object detection model performance. In particular, example aspects of the present disclosure are directed to iterative reinforcement learning approaches in which, at each of a plurality of iterations, a controller model selects a series of one or more augmentation operations to be applied to training images to generate augmented images. For example, the controller model can select the augmentation operations from a defined search space of available operations which can, for example, include operations that augment the training image without modification of the locations of a target object and corresponding bounding shape within the image and/or operations that do modify the locations of the target object and bounding shape within the training image.

公开(公告)号：US20210097348A1

公开(公告)日：2021-04-01

申请号：US16833449

申请日：2020-03-27

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.

公开(公告)号：US20200065689A1

公开(公告)日：2020-02-27

申请号：US16674801

申请日：2019-11-05

Applicant: Google LLC

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

IPC: G06N5/04 ,  G06N3/04 ,  G06N3/08 ,  G06T7/00

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：US10521729B2

公开(公告)日：2019-12-31

申请号：US16040067

申请日：2018-07-19

Applicant: Google LLC

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

IPC: G06N5/04 ,  G06N3/08 ,  G06T7/00 ,  G06N3/04

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：US20190251439A1

公开(公告)日：2019-08-15

申请号：US16397641

申请日：2019-04-29

Applicant: Google LLC

Inventor： Barret Zoph ,  Quoc V. Le

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/08 ,  G06N3/0445 ,  G06N3/0454

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：US20190026639A1

公开(公告)日：2019-01-24

申请号：US16040067

申请日：2018-07-19

Applicant: Google LLC

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

IPC: G06N5/04 ,  G06N3/08 ,  G06T7/00

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：US20250053815A1

公开(公告)日：2025-02-13

申请号：US18806647

申请日：2024-08-15

Applicant: Google LLC

Inventor： William Bradley Fedus ,  Barret Zoph ,  Noam M. Shazeer

IPC: G06N3/082 ,  G06N3/045

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a machine learning task on a network input to generate a network output. In one aspect, one of the systems includes a neural network configured to perform the machine learning task, the neural network including one or more switch layers.

公开(公告)号：US20240256965A1

公开(公告)日：2024-08-01

申请号：US18424624

申请日：2024-01-26

Applicant: Google LLC

Inventor： Hyung Won Chung ,  Barret Zoph ,  Dengyong Zhou ,  Liam Fedus ,  Shayne Longpre ,  Le Hou ,  Yi Tay ,  Jason Weng Wei ,  Siddhartha Brahma ,  Quoc V. Le

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: An example method for training a machine-learned sequence processing model includes obtaining a plurality of training examples for training the machine-learned sequence processing model. For each respective training example of the plurality of training examples, the example method includes: obtaining a respective query associated with the respective training example; inputting the respective query to the machine-learned sequence processing model; obtaining, from the machine-learned sequence processing model a response to the respective query and a trace of intermediate states from the respective query to the response; evaluating the response using a ground truth response associated with the respective training example; evaluating the trace using a ground truth trace associated with the respective training example; and updating one or more parameters of the machine-learned sequence processing model based on the evaluation of the response and based on the evaluation of the trace.

公开(公告)号：US12033038B2

公开(公告)日：2024-07-09

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

CPC classification number: 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：US20220012537A1

公开(公告)日：2022-01-13

申请号：US17487548

申请日：2021-09-28

Applicant: Google LLC

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

IPC: G06K9/62 ,  G10L15/16 ,  G10L15/28 ,  G10L15/06 ,  G10L15/12 ,  G06N20/00

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.