公开(公告)号：US20220391778A1

公开(公告)日：2022-12-08

申请号：US17770919

申请日：2019-10-23

Applicant: Google LLC

Inventor： Bradley Ray Green ,  Shawn Ryan O'Banion

IPC: G06N20/20

Abstract: The present disclosure provides for the generation of embeddings within a machine learning framework, such as, for example, a federated learning framework in which a high-quality centralized model is trained on training data distributed over a large number of clients each with unreliable network connections and low computational power. In an example federated learning setting, in each of a plurality of rounds, each client independently updates the model based on its local data and communicates the updated model back to the server, where all the client-side updates are used to update a global model. The present disclosure provides systems and methods that may generate embeddings with local training data while preserving the privacy of a user of the client device.

公开(公告)号：US20230281979A1

公开(公告)日：2023-09-07

申请号：US18006078

申请日：2020-08-03

Applicant: Xuhui JIA ,  Raviteja VEMULAPALLI ,  Yukun ZHU ,  Bradley Ray GREEN ,  Bardia DOOSTI ,  Ching-Hui CHEN ,  Google LLC

Inventor： Xuhui Jia ,  Raviteja Vemulapalli ,  Bradley Ray Green ,  Bardia Doosti ,  Ching-Hui Chen

IPC: G06V10/82 ,  G06V10/776

CPC classification number: G06V10/82 ,  G06V10/776

Abstract: Systems and methods of the present disclosure are directed to a method for training a machine-learned visual attention model. The method can include obtaining image data that depicts a head of a person and an additional entity. The method can include processing the image data with an encoder portion of the visual attention model to obtain latent head and entity encodings. The method can include processing the latent encodings with the visual attention model to obtain a visual attention value and processing the latent encodings with a machine-learned visual location model to obtain a visual location estimation. The method can include training the models by evaluating a loss function that evaluates differences between the visual location estimation and a pseudo visual location label derived from the image data and between the visual attention value and a ground truth visual attention label.

公开(公告)号：US20230222628A1

公开(公告)日：2023-07-13

申请号：US17572923

申请日：2022-01-11

Applicant: Google LLC

Inventor： Yang Zhao ,  Yu-Chuan Su ,  Chun-Te Chu ,  Yandong Li ,  Marius Renn ,  Yukun Zhu ,  Xuhui Jia ,  Bradley Ray Green

IPC: G06T5/00 ,  G06V40/16

CPC classification number: G06T5/001 ,  G06V40/168 ,  G06T2207/30201 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: Systems and methods for training a restoration model can leverage training for two sub-tasks to train the restoration model to generate realistic and identity-preserved outputs. The systems and methods can balance the training of the generation task and the reconstruction task to ensure the generated outputs preserve the identity of the original subject while generating realistic outputs. The systems and methods can further leverage a feature quantization model and skip connections to improve the model output and overall training.

公开(公告)号：US20230214656A1

公开(公告)日：2023-07-06

申请号：US18009629

申请日：2020-06-10

Applicant: Google LLC

Inventor： Raviteja Vemulapalli ,  Jianrui Cai ,  Bradley Ray Green ,  Ching-Hui Chen ,  Lior Shapira

IPC: G06N3/082 ,  G06V10/82 ,  G06V10/764

CPC classification number: G06N3/082 ,  G06V10/82 ,  G06V10/764

Abstract: At training time, a base neural network can be trained to perform each of a plurality of basis subtasks included in a total set of basis subtasks (e.g., individually or some combination thereof). Next, a description of a desired combined subtask can be obtained. Based on the description of the combined subtask, a mask generator can produce a pruning mask which is used to prune the base neural network into a smaller combined-subtask-specific network that performs only the two or more basis subtasks included in the combined subtask.