公开(公告)号：US20240127075A1

公开(公告)日：2024-04-18

申请号：US18212629

申请日：2023-06-21

Applicant: NVIDIA Corporation

Inventor： Shalini De Mello ,  Christian Jacobsen ,  Xunlei Wu ,  Stephen Tyree ,  Alice Li ,  Wonmin Byeon ,  Shangru Li

IPC: G06N3/0985

CPC classification number: G06N3/0985

Abstract: Machine learning is a process that learns a model from a given dataset, where the model can then be used to make a prediction about new data. In order to reduce the costs associated with collecting and labeling real world datasets for use in training the model, computer processes can synthetically generate datasets which simulate real world data. The present disclosure improves the effectiveness of such synthetic datasets for training machine learning models used in real world applications, in particular by generating a synthetic dataset that is specifically targeted to a specified downstream task (e.g. a particular computer vision task, a particular natural language processing task, etc.).

公开(公告)号：US20220237336A1

公开(公告)日：2022-07-28

申请号：US17156408

申请日：2021-01-22

Applicant: NVIDIA Corporation

Inventor： Zeyu Zhao ,  Shangru Li ,  Parthasarathy Sriram ,  Farzin Aghdasi

IPC: G06F30/20 ,  G06K9/62 ,  G06N3/08 ,  G06T11/20

Abstract: Systems and methods disclosed relate to generating training data. In one embodiment, the disclosure relates to systems and methods for generating training data to train a neural network to detect and classify objects. A simulator obtains 3D models of objects, and simulates 3D environments comprising the objects using physics-based simulations. The simulations may include applying real-world physical conditions, such as gravity, friction, and the like on the objects. The system may generate images of the simulations, and use the images to train a neural network to detect and classify the objects from images.