公开(公告)号：US11816185B1

公开(公告)日：2023-11-14

申请号：US16383347

申请日：2019-04-12

Applicant: Nvidia Corporation

Inventor： Holger Roth ,  Yingda Xia ,  Dong Yang ,  Daguang Xu

IPC: G06F18/214 ,  G06F9/30 ,  G06N3/08 ,  G16H30/40 ,  G06N5/04 ,  G06F18/211 ,  G06F18/2433 ,  G06N3/045

CPC classification number: G06F18/2155 ,  G06F9/3001 ,  G06F18/211 ,  G06F18/2433 ,  G06N3/045 ,  G06N3/08 ,  G06N5/04 ,  G16H30/40 ,  G06V2201/031

Abstract: Volumetric quantification can be performed for various parameters of an object represented in volumetric data. Multiple views of the object can be generated, and those views provided to a set of neural networks that can generate inferences in parallel. The inferences from the different networks can be used to generate pseudo-labels for the data, for comparison purposes, which enables a co-training loss to be determined for the unlabeled data. The co-training loss can then be used to update the relevant network parameters for the overall data analysis network. If supervised data is also available then the network parameters can further be updated using the supervised loss.

公开(公告)号：US20210334955A1

公开(公告)日：2021-10-28

申请号：US16858219

申请日：2020-04-24

Applicant: Nvidia Corporation

Inventor： Holger Roth ,  Dong Yang ,  Daguang Xu ,  Vishwesh Nath

IPC: G06T7/00 ,  G06F40/169 ,  G06N3/08 ,  G06T7/11

Abstract: Apparatuses, systems, and techniques are presented to predict annotations for objects in images. In at least one embodiment, one or more annotations corresponding to one or more objects within one or more images are generated based, at least in part, on one or more neural networks iteratively trained using the one or more images.

公开(公告)号：US20210334975A1

公开(公告)日：2021-10-28

申请号：US16856823

申请日：2020-04-23

Applicant: Nvidia Corporation

Inventor： Dong Yang ,  Holger Roth ,  Xiaosong Wang ,  Ziyue Xu ,  Andriy Myronenko ,  Daguang Xu

IPC: G06T7/11 ,  G06T7/12 ,  G06N3/04

Abstract: Apparatuses, systems, and techniques are presented to predict segmentations for objects in images. In at least one embodiment, a neural network is trained to determine one or more segmentation masks corresponding to one or more objects of one or more digital images based, at least in part, on one or more boundary regions of the one or more objects.

公开(公告)号：US12164599B1

公开(公告)日：2024-12-10

申请号：US18232202

申请日：2023-08-09

Applicant: NVIDIA Corporation

Inventor： Holger Roth ,  Yingda Xia ,  Dong Yang ,  Daguang Xu

IPC: G06F18/214 ,  G06F9/30 ,  G06F18/211 ,  G06F18/2433 ,  G06N3/045 ,  G06N3/08 ,  G06N5/04 ,  G16H30/40

Abstract: Volumetric quantification can be performed for various parameters of an object represented in volumetric data. Multiple views of the object can be generated, and those views provided to a set of neural networks that can generate inferences in parallel. The inferences from the different networks can be used to generate pseudo-labels for the data, for comparison purposes, which enables a co-training loss to be determined for the unlabeled data. The co-training loss can then be used to update the relevant network parameters for the overall data analysis network. If supervised data is also available then the network parameters can further be updated using the supervised loss.

公开(公告)号：US20230069310A1

公开(公告)日：2023-03-02

申请号：US17398655

申请日：2021-08-10

Applicant: Nvidia Corporation

Inventor： Andriy Myronenko ,  Ziyue Xu ,  Dong Yang ,  Holger Roth ,  Daguang Xu

IPC: G06T7/11 ,  G06K9/62 ,  G06N3/08

Abstract: Apparatuses, systems, and techniques are presented to classify objects in images. In at least one embodiment, one or more neural networks are used to identify one or more objects in one or more full images based, at least in part, on the one or more neural networks having been trained using the one or more full images and one or more portions of the one or more full images.

公开(公告)号：US20230061998A1

公开(公告)日：2023-03-02

申请号：US17459644

申请日：2021-08-27

Applicant: Nvidia Corporation

Inventor： Dong Yang ,  Andriy Myronenko ,  Xiaosong Wang ,  Ziyue Xu ,  Holger Roth ,  Daguang Xu

IPC: G06K9/62 ,  G06N3/08 ,  G06N3/04 ,  G06F7/24

Abstract: Apparatuses, systems, and techniques are presented to select neural networks. In at least one embodiment, one or more first neural networks can be used to select one or more second neural networks, as may be based at least in part upon an inference to be generated by the one or more second neural networks.