公开(公告)号：US20220405583A1

公开(公告)日：2022-12-22

申请号：US17681632

申请日：2022-02-25

Applicant: NVIDIA CORPORATION

Inventor： Arash VAHDAT ,  Karsten KREIS ,  Jan KAUTZ

IPC: G06N3/08

Abstract: One embodiment of the present invention sets forth a technique for training a generative model. The technique includes converting a first data point included in a training dataset into a first set of values associated with a base distribution for a score-based generative model. 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 performing one or more additional operations to convert the first set of latent variable values into a second data point. Finally, the technique includes computing one or more losses based on the first data point and the second data point and generating a trained generative model based on the one or more losses, wherein the trained generative model includes the score-based generative model.

公开(公告)号：US20190102908A1

公开(公告)日：2019-04-04

申请号：US16152303

申请日：2018-10-04

Applicant: NVIDIA Corporation

Inventor： Xiaodong YANG ,  Xitong YANG ,  Fanyi XIAO ,  Ming-Yu LIU ,  Jan KAUTZ

IPC: G06T7/73

Abstract: Iterative prediction systems and methods for the task of action detection process an inputted sequence of video frames to generate an output of both action tubes and respective action labels, wherein the action tubes comprise a sequence of bounding boxes on each video frame. An iterative predictor processes large offsets between the bounding boxes and the ground-truth.