公开(公告)号：US20240320912A1

公开(公告)日：2024-09-26

申请号：US18611236

申请日：2024-03-20

Applicant: Google LLC

Inventor： Yuanzhen Li ,  Amit Raj ,  Varun Jampani ,  Benjamin Joseph Mildenhall ,  Benjamin Michael Poole ,  Jonathan Tilton Barron ,  Kfir Aberman ,  Michael Niemeyer ,  Michael Rubinstein ,  Nataniel Ruiz Gutierrez ,  Shiran Elyahu Zada ,  Srinivas Kaza

IPC: G06T17/00 ,  H04N13/279 ,  H04N13/351

CPC classification number: G06T17/00 ,  H04N13/279 ,  H04N13/351

Abstract: A fractional training process can be performed training images to an instance of a machine-learned generative image model to obtain a partially trained instance of the model. A fractional optimization process can be performed with the partially trained instance to an instance of a machine-learned three-dimensional (3D) implicit representation model obtain a partially optimized instance of the model. Based on the plurality of training images, pseudo multi-view subject images can be generated with the partially optimized instance of the 3D implicit representation model and a fully trained instance of the generative image model; The partially trained instance of the model can be trained with a set of training data. The partially optimized instance of the machine-learned 3D implicit representation model can be trained with the machine-learned multi-view image model.

公开(公告)号：US20230059708A1

公开(公告)日：2023-02-23

申请号：US17797966

申请日：2021-02-08

Applicant: Google LLC

Inventor： Luke Shekerjian Metz ,  Ruoxi Sun ,  Christian Daniel Freeman ,  Benjamin Michael Poole ,  Niru Maheswaranathan ,  Jascha Narain Sohl-Dickstein

IPC: G06N3/08

Abstract: The present disclosure provides a computer-implemented method for determining an optimized list of sets of hyperparameter values for application to an additional machine learning task. The method includes obtaining data describing a plurality of different machine learning tasks. The method includes obtaining a plurality of candidate sets of hyperparameter values. The method includes determining an ordered list of sets of hyperparameters selected from the plurality of candidate sets of hyperparameter values, wherein the ordered list of sets of hyperparameters minimizes an aggregate loss over the plurality of different machine learning tasks. The method includes storing the ordered list of sets of hyperparameters for use in training an additional machine learning model to perform an additional machine learning task.