公开(公告)号：US20230245435A1

公开(公告)日：2023-08-03

申请号：US17589535

申请日：2022-01-31

Applicant: Samsung Electronics Co., Ltd.

Inventor： Changsheng Zhao ,  Burak Uzkent ,  Yilin Shen ,  Hongxia Jin

IPC: G06V10/80 ,  G06V10/778 ,  G06V10/774 ,  G06F40/279

CPC classification number: G06V10/811 ,  G06V10/778 ,  G06V10/774 ,  G06F40/279

Abstract: A method includes obtaining a batch of training data including multiple paired image-text pairs and multiple unpaired image-text pairs, where each paired image-text pair and each unpaired image-text pair includes an image and a text. The method also includes training a machine learning model using the training data based on an optimization of a combination of losses. The losses include, for each paired image-text pair, (i) a first multi-modal representation loss based on the paired image-text pair and (ii) a second multi-modal representation loss based on two or more unpaired image-text pairs, selected from among the multiple unpaired image-text pairs, wherein each of the two or more unpaired image-text pairs includes either the image or the text of the paired image-text pair.

公开(公告)号：US20210383272A1

公开(公告)日：2021-12-09

申请号：US17166908

申请日：2021-02-03

Applicant: Samsung Electronics Co., Ltd.

Inventor： Ting Hua ,  Yilin Shen ,  Changsheng Zhao ,  Hongxia Jin

IPC: G06N20/00 ,  G06K9/62

Abstract: A continual learning method includes obtaining an input data including a trained model, continual learning (CL) Information, and training data by an electronic device. The method also includes re-training, using the electronic device, the model for a task based on the training data. The method also includes updating, using the electronic device, the CL Information based on the model and the training data. The method further includes selecting a first set of exemplars from the training data based on data associated with the CL Information. The CL Information includes a first group of variables associated with the model and a second group of variables associated with the model that changes to the first group of variables have stronger impact to the model's performance of the task than changes to the second group of variables.

公开(公告)号：US12183062B2

公开(公告)日：2024-12-31

申请号：US17589535

申请日：2022-01-31

Applicant: Samsung Electronics Co., Ltd.

Inventor： Changsheng Zhao ,  Burak Uzkent ,  Yilin Shen ,  Hongxia Jin

IPC: G06V10/80 ,  G06F40/279 ,  G06V10/774 ,  G06V10/778

Abstract: A method includes obtaining a batch of training data including multiple paired image-text pairs and multiple unpaired image-text pairs, where each paired image-text pair and each unpaired image-text pair includes an image and a text. The method also includes training a machine learning model using the training data based on an optimization of a combination of losses. The losses include, for each paired image-text pair, (i) a first multi-modal representation loss based on the paired image-text pair and (ii) a second multi-modal representation loss based on two or more unpaired image-text pairs, selected from among the multiple unpaired image-text pairs, wherein each of the two or more unpaired image-text pairs includes either the image or the text of the paired image-text pair.

公开(公告)号：US20220114479A1

公开(公告)日：2022-04-14

申请号：US17090542

申请日：2020-11-05

Applicant: Samsung Electronics Co., Ltd.

Inventor： Changsheng Zhao ,  Yilin Shen ,  Hongxia Jin

IPC: G06N20/00 ,  G06N7/00 ,  G06N5/04

Abstract: A machine learning method using a trained machine learning model residing on an electronic device includes receiving an inference request by the electronic device. The method also includes determining, using the trained machine learning model, an inference result for the inference request using a selected inference path in the trained machine learning model. The selected inference path is selected based on a highest probability for each layer of the trained machine learning model. A size of the trained machine learning model is reduced corresponding to constraints imposed by the electronic device. The method further includes executing an action in response to the inference result.