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公开(公告)号：US10817805B2
公开(公告)日：2020-10-27
申请号：US16417133
申请日：2019-05-20
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.
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公开(公告)号：US20190354808A1
公开(公告)日：2019-11-21
申请号：US16416888
申请日：2019-05-20
Applicant: Google LLC
Inventor： Daniel Sung-Joon Park , Quoc Le , William Chan , Ekin Dogus Cubuk , Barret Zoph , Yu Zhang , Chung-Cheng Chiu
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.
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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.
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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
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.
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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.
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Patent Agency Ranking
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
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.
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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
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.
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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
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.
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公开(公告)号：US20240242125A1
公开(公告)日：2024-07-18
申请号：US18584625
申请日：2024-02-22
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.
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公开(公告)号：US11816577B2
公开(公告)日：2023-11-14
申请号：US17487548
申请日：2021-09-28
Applicant: Google LLC
Inventor： Daniel Sung-Joon Park , Quoc Le , William Chan , Ekin Dogus Cubuk , Barret Zoph , Yu Zhang , Chung-Cheng Chiu
IPC: G10L15/06 , G10L15/12 , G06N3/084 , G10L15/16 , G10L15/28 , G06N20/00 , G06F18/214 , G06V10/774 , G06V10/82
CPC classification number: G06N3/084 , G06F18/2148 , G06N20/00 , G06V10/7747 , G06V10/82 , G10L15/063 , G10L15/12 , G10L15/16 , G10L15/28
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.
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