公开(公告)号：US20240126811A1

公开(公告)日：2024-04-18

申请号：US18098015

申请日：2023-01-17

Applicant: NVIDIA Corporation

Inventor： Marc Teva Law ,  James Robert Lucas

IPC: G06F16/901

CPC classification number: G06F16/9024

Abstract: Apparatuses, systems, and techniques to indicate data dependencies. In at least one embodiment, one or more neural networks are used to generate one or more indicators of one or more data dependencies and one or more indicators of direction of the one or more data dependencies.

公开(公告)号：US20210374489A1

公开(公告)日：2021-12-02

申请号：US17226561

申请日：2021-04-09

Applicant: Nvidia Corporation

Inventor： Aayush Prakash ,  Shoubhik Debnath ,  Jean-Francois Lafleche ,  Eric Cameracci ,  Gavriel State ,  Marc Teva Law

IPC: G06K9/62 ,  G06K9/00

Abstract: Approaches are presented for training and using scene graph generators for transfer learning. A scene graph generation technique can decompose a domain gap into individual types of discrepancies, such as may relate to appearance, label, and prediction discrepancies. These discrepancies can be reduced, at least in part, by aligning the corresponding latent and output distributions using one or more gradient reversal layers (GRLs). Label discrepancies can be addressed using self-pseudo-statistics collected from target data. Pseudo statistic-based self-learning and adversarial techniques can be used to manage these discrepancies without the need for costly supervision from a real-world dataset.

公开(公告)号：US20250111201A1

公开(公告)日：2025-04-03

申请号：US18477184

申请日：2023-09-28

Applicant: Nvidia Corporation

Inventor： James Robert Lucas ,  Derek Lim ,  Haggai Maron ,  Marc Teva Law

IPC: G06N3/0455 ,  G06N3/08

Abstract: Embodiments are disclosed for a generating graph representations of neural networks to be used as input for one or more metanetworks. Architectural information can be extracted from a neural network and used to generate graph a representation. A subgraph can be generated for each layer of the neural network, where each subgraph includes nodes that correspond to neurons and connecting edges that correspond to weights. Each layer of the neural network can be associated with a bias node that is connected to individual nodes of that layer using edges representing bias weights. Various types of neural networks and layers of neural networks can be represented by such graphs, which are then used as inputs for metanetworks. The subgraphs can be combined into a comprehensive graph representation of the neural network, which can be provided as input to a metanetwork to generate network parameters or perform another such operation.

公开(公告)号：US11574155B2

公开(公告)日：2023-02-07

申请号：US17226561

申请日：2021-04-09

Applicant: Nvidia Corporation

Inventor： Aayush Prakash ,  Shoubhik Debnath ,  Jean-Francois Lafleche ,  Eric Cameracci ,  Gavriel State ,  Marc Teva Law

IPC: G06K9/62 ,  G06V20/00 ,  G06V10/82 ,  G06V20/70 ,  G06V20/56

Abstract: Approaches are presented for training and using scene graph generators for transfer learning. A scene graph generation technique can decompose a domain gap into individual types of discrepancies, such as may relate to appearance, label, and prediction discrepancies. These discrepancies can be reduced, at least in part, by aligning the corresponding latent and output distributions using one or more gradient reversal layers (GRLs). Label discrepancies can be addressed using self-pseudo-statistics collected from target data. Pseudo statistic-based self-learning and adversarial techniques can be used to manage these discrepancies without the need for costly supervision from a real-world dataset.

公开(公告)号：US20240320993A1

公开(公告)日：2024-09-26

申请号：US18673785

申请日：2024-05-24

Applicant: Nvidia Corporation

Inventor： Aayush Prakash ,  Shoubhik Debnath ,  Jean-Francois Lafleche ,  Eric Cameracci ,  Gavriel State ,  Marc Teva Law

IPC: G06V20/70 ,  G06F18/10 ,  G06F18/20 ,  G06F18/24 ,  G06V10/764 ,  G06V10/82 ,  G06V10/84 ,  G06V20/00 ,  G06V20/56

CPC classification number: G06V20/70 ,  G06F18/10 ,  G06F18/24 ,  G06F18/29 ,  G06V10/764 ,  G06V10/82 ,  G06V10/84 ,  G06V20/00 ,  G06V20/56

Abstract: Approaches are presented for training and using scene graph generators for transfer learning. A scene graph generation technique can decompose a domain gap into individual types of discrepancies, such as may relate to appearance, label, and prediction discrepancies. These discrepancies can be reduced, at least in part, by aligning the corresponding latent and output distributions using one or more gradient reversal layers (GRLs). Label discrepancies can be addressed using self-pseudo-statistics collected from target data. Pseudo statistic-based self-learning and adversarial techniques can be used to manage these discrepancies without the need for costly supervision from a real-world dataset.

公开(公告)号：US11995883B2

公开(公告)日：2024-05-28

申请号：US18163482

申请日：2023-02-02

Applicant: Nvidia Corporation

Inventor： Aayush Prakash ,  Shoubhik Debnath ,  Jean-Francois Lafleche ,  Eric Cameracci ,  Gavriel State ,  Marc Teva Law

IPC: G06V10/82 ,  G06F18/10 ,  G06F18/20 ,  G06F18/24 ,  G06V10/764 ,  G06V10/84 ,  G06V20/00 ,  G06V20/56 ,  G06V20/70

CPC classification number: G06V10/82 ,  G06F18/10 ,  G06F18/24 ,  G06F18/29 ,  G06V10/764 ,  G06V10/84 ,  G06V20/00 ,  G06V20/70 ,  G06V20/56

Abstract: Approaches are presented for training and using scene graph generators for transfer learning. A scene graph generation technique can decompose a domain gap into individual types of discrepancies, such as may relate to appearance, label, and prediction discrepancies. These discrepancies can be reduced, at least in part, by aligning the corresponding latent and output distributions using one or more gradient reversal layers (GRLs). Label discrepancies can be addressed using self-pseudo-statistics collected from target data. Pseudo statistic-based self-learning and adversarial techniques can be used to manage these discrepancies without the need for costly supervision from a real-world dataset.

公开(公告)号：US20230177826A1

公开(公告)日：2023-06-08

申请号：US18163482

申请日：2023-02-02

Applicant: Nvidia Corporation

Inventor： Aayush Prakash ,  Shoubhik Debnath ,  Jean-Francois Lafleche ,  Eric Cameracci ,  Gavriel State ,  Marc Teva Law

IPC: G06V10/82 ,  G06V20/70 ,  G06F18/10 ,  G06F18/24 ,  G06F18/20 ,  G06V10/764 ,  G06V10/84 ,  G06V20/00

CPC classification number: G06V10/82 ,  G06V20/70 ,  G06F18/10 ,  G06F18/24 ,  G06F18/29 ,  G06V10/764 ,  G06V10/84 ,  G06V20/00 ,  G06V20/56

Abstract: Approaches are presented for training and using scene graph generators for transfer learning. A scene graph generation technique can decompose a domain gap into individual types of discrepancies, such as may relate to appearance, label, and prediction discrepancies. These discrepancies can be reduced, at least in part, by aligning the corresponding latent and output distributions using one or more gradient reversal layers (GRLs). Label discrepancies can be addressed using self-pseudo-statistics collected from target data. Pseudo statistic-based self-learning and adversarial techniques can be used to manage these discrepancies without the need for costly supervision from a real-world dataset.