-
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.
-
公开(公告)号：US12265911B2
公开(公告)日：2025-04-01
申请号：US17121161
申请日：2020-12-14
Applicant: Google LLC
Inventor： Gamaleldin Elsayed , Prajit Ramachandran , Jon Shlens , Simon Kornblith
Abstract: A computing system can include one or more non-transitory computer-readable media that collectively store a neural network including one or more layers with relaxed spatial invariance. Each of the one or more layers can be configured to receive a respective layer input. Each of the one or more layers can be configured to convolve a plurality of different kernels against the respective layer input to generate a plurality of intermediate outputs, each of the plurality of intermediate outputs having a plurality of portions. Each of the one or more layers can be configured to apply, for each of the plurality of intermediate outputs, a respective plurality of weights respectively associated with the plurality of portions to generate a respective weighted output. Each of the one or more layers can be configured to generate a respective layer output based on the weighted outputs.
-
Patent Agency Ranking
公开(公告)号：US11682191B2
公开(公告)日：2023-06-20
申请号：US17702438
申请日：2022-03-23
Applicant: Google LLC
Inventor： Jon Shlens , Ekin Dogus Cubuk , Quoc Le , Tsung-Yi Lin , Barret Zoph , Golnaz Ghiasi
CPC classification number: G06V10/772 , G06F18/217 , G06F18/24 , G06T3/20 , G06T3/60 , G06T11/001
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：US20190354817A1
公开(公告)日：2019-11-21
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.
-
公开(公告)号：US20230274532A1
公开(公告)日：2023-08-31
申请号：US18313772
申请日：2023-05-08
Applicant: Google LLC
Inventor： Jon Shlens , Ekin Dogus Cubuk , Quoc Le , Tsung-Yi Lin , Barret Zoph , Golnaz Ghiasi
CPC classification number: G06V10/772 , G06F18/217 , G06F18/24 , G06T3/20 , G06T3/60 , G06T11/001
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：US20220215682A1
公开(公告)日：2022-07-07
申请号：US17702438
申请日：2022-03-23
Applicant: Google LLC
Inventor： Jon Shlens , Ekin Dogus Cubuk , Quoc Le , Tsung-Yi Lin , Barret Zoph , Golnaz Ghiasi
IPC: G06V30/194 , G06K9/62 , G06T3/60 , G06T3/20 , G06T11/00
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：US20210248472A1
公开(公告)日：2021-08-12
申请号：US17121161
申请日：2020-12-14
Applicant: Google LLC
Inventor： Gamaleldin Elsayed , Prajit Ramachandran , Jon Shlens , Simon Kornblith
Abstract: The present disclosure provides a neural network including one or more layers with relaxed spatial invariance. Each of the one or more layers can be configured to receive a respective layer input. Each of the one or more layers can be configured to convolve a plurality of different kernels against the respective layer input to generate a plurality of intermediate outputs, each of the plurality of intermediate outputs having a plurality of portions. Each of the one or more layers can be configured to apply, for each of the plurality of intermediate outputs, a respective plurality of weights respectively associated with the plurality of portions to generate a respective weighted output. Each of the one or more layers can be configured to generate a respective layer output based on the weighted outputs.
-
-
-
-
-
-
Search Results
7
records
(took 0.009 seconds)
