公开(公告)号：US20240412491A1

公开(公告)日：2024-12-12

申请号：US18207953

申请日：2023-06-09

Applicant: NVIDIA Corporation

Inventor： Shagan Sah ,  Nishant Puri ,  Yuzhuo Ren ,  Rajath Bellipady Shetty ,  Weili Nie ,  Arash Vahdat ,  Animashree Anandkumar

IPC: G06V10/776 ,  G06N3/094 ,  G06T11/00 ,  G06V10/75 ,  G06V10/774 ,  G06V10/82 ,  G06V40/16

Abstract: Apparatuses, system, and techniques use one or more first neural networks to generate one or more synthetic data to train one or more second neural networks based, at least in part, on one or more performance metrics of one or more second neural networks.

公开(公告)号：US20240253217A1

公开(公告)日：2024-08-01

申请号：US18538248

申请日：2023-12-13

Applicant: NVIDIA Corporation

Inventor： Arash Vahdat ,  Hongxu Yin ,  Jan Kautz ,  Jiaming Song ,  Ming-Yu Liu ,  Morteza Mardani ,  Qinsheng Zhang

IPC: B25J9/16

CPC classification number: B25J9/163 ,  B25J9/1664 ,  B25J9/1697

Abstract: Apparatuses, systems, and techniques to calculate a combined loss value based on applying one or more loss functions to the plurality of samples generated by a diffusion model to update the samples to determine a synthesized motions of one or more objects.

公开(公告)号：US20240005604A1

公开(公告)日：2024-01-04

申请号：US18320716

申请日：2023-05-19

Applicant: Nvidia Corporation

Inventor： Karsten Julian Kreis ,  Xiaohui Zeng ,  Arash Vahdat ,  Francis Williams ,  Zan Gojcic ,  Or Litany ,  Sanja Fidler

IPC: G06T17/20 ,  G06V10/44

CPC classification number: G06T17/20 ,  G06V10/44

Abstract: Approaches presented herein provide for the unconditional generation of novel three dimensional (3D) object shape representations, such as point clouds or meshes. In at least one embodiment, a first denoising diffusion model (DDM) can be trained to synthesize a 1D shape latent from Gaussian noise, and a second DDM can be trained to generate a set of latent points conditioned on this 1D shape latent. The shape latent and set of latent points can be provided to a decoder to generate a 3D point cloud representative of a random object from among the object classes on which the models were trained. A surface reconstruction process may be used to generate a surface mesh from this generated point cloud. Such an approach can scale to complex and/or multimodal distributions, and can be highly flexible as it can be adapted to various tasks such as multimodal voxel- or text-guided synthesis.

公开(公告)号：US20230377099A1

公开(公告)日：2023-11-23

申请号：US18319986

申请日：2023-05-18

Applicant: Nvidia Corporation

Inventor： Karsten Julian Kreis ,  Tim Dockhorn ,  Arash Vahdat

IPC: G06T5/00 ,  G06T7/64 ,  G06T11/00 ,  G06N3/045 ,  G06N3/08

CPC classification number: G06T5/002 ,  G06T7/64 ,  G06T11/00 ,  G06N3/045 ,  G06N3/08 ,  G06T2207/20084 ,  G06T2207/20081 ,  G06T2207/30241 ,  G06T2200/28

Abstract: Approaches presented herein provide for the generation of synthesized data from input noise using a denoising diffusion network. A higher order differential equation solver can be used for the denoising process, with one or more higher-order terms being distilled into one or more separate efficient neural networks. A separate, efficient neural network can be called together with a primary denoising model at inference time without significant loss in sampling efficiency. The separate neural network can provide information about the curvature (or other higher-order term) of the differential equation, representing a denoising trajectory, that can be used by the primary diffusion network to denoise the image using fewer denoising iterations.

公开(公告)号：US20230015253A1

公开(公告)日：2023-01-19

申请号：US17505384

申请日：2021-10-19

Applicant: Nvidia Corporation

Inventor： Weili Nie ,  Arash Vahdat ,  Anima Anandkumar

IPC: G06N3/08

Abstract: Apparatuses, systems, and techniques are presented to generate one or more images comprising one or more objects based, at least in part, on one or more dynamically configurable attributes of the one or objects. In at least one embodiment, one or more images comprising one or more objects can be generated based, at least in part, on one or more dynamically configurable attributes of the one or objects.

公开(公告)号：US20220318557A1

公开(公告)日：2022-10-06

申请号：US17224041

申请日：2021-04-06

Applicant: NVIDIA Corporation

Inventor： Sina Mohseni ,  Arash Vahdat ,  Jay Yadawa

IPC: G06K9/00 ,  G06K9/62 ,  G06N3/08 ,  G06N3/04

Abstract: Apparatuses, systems, and techniques to identify out-of-distribution input data in one or more neural networks. In at least one embodiment, a technique includes training one or more neural networks to infer a plurality of characteristics about input information based, at least in part, on the one or more neural networks being independently trained to infer each of the plurality of characteristics about the input information.

公开(公告)号：US20220108213A1

公开(公告)日：2022-04-07

申请号：US17317698

申请日：2021-05-11

Applicant: NVIDIA Corporation

Inventor： Tianshi Cao ,  Alex Bie ,  Karsten Julian Kreis ,  Sanja Fidler ,  Arash Vahdat

IPC: G06N20/00 ,  G06K9/62 ,  G06N3/08 ,  G06F21/62

Abstract: Apparatuses, systems, and techniques to train a generative model based at least in part on a private dataset. In at least one embodiment, the generative model is trained based at least in part on a differentially private Sinkhorn algorithm, for example, using backpropagation with gradient descent to determine a gradient of a set of parameters of the generative models and modifying the set of parameters based at least in part on the gradient.

公开(公告)号：US20210073612A1

公开(公告)日：2021-03-11

申请号：US16566797

申请日：2019-09-10

Applicant: NVIDIA Corporation

Inventor： Arash Vahdat ,  Arun Mohanray Mallya ,  Ming-Yu Liu ,  Jan Kautz

IPC: G06N3/04 ,  G06F7/57 ,  G06N3/063 ,  G06N3/08

Abstract: In at least one embodiment, differentiable neural architecture search and reinforcement learning are combined under one framework to discover network architectures with desired properties such as high accuracy, low latency, or both. In at least one embodiment, an objective function for search based on generalization error prevents the selection of architectures prone to overfitting.

公开(公告)号：US12249048B2

公开(公告)日：2025-03-11

申请号：US17681625

申请日：2022-02-25

Applicant: NVIDIA CORPORATION

Inventor： Arash Vahdat ,  Karsten Kreis ,  Jan Kautz

IPC: G06T5/70 ,  G06N3/08 ,  G06V10/82

Abstract: One embodiment of the present invention sets forth a technique for generating data. The technique includes sampling from a first distribution associated with the score-based generative model to generate a first set of values. The technique also includes performing one or more denoising operations via the score-based generative model to convert the first set of values into a first set of latent variable values associated with a latent space. The technique further includes converting the first set of latent variable values into a generative output.

公开(公告)号：US12175350B2

公开(公告)日：2024-12-24

申请号：US16566797

申请日：2019-09-10

Applicant: NVIDIA Corporation

Inventor： Arash Vahdat ,  Arun Mohanray Mallya ,  Ming-Yu Liu ,  Jan Kautz

IPC: G06N20/00 ,  G06F7/57 ,  G06N3/04 ,  G06N3/063 ,  G06N3/08 ,  G05B13/02

Abstract: In at least one embodiment, differentiable neural architecture search and reinforcement learning are combined under one framework to discover network architectures with desired properties such as high accuracy, low latency, or both. In at least one embodiment, an objective function for search based on generalization error prevents the selection of architectures prone to overfitting.