公开(公告)号：US11463750B2

公开(公告)日：2022-10-04

申请号：US16612498

申请日：2018-09-28

申请人： KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY

发明人： Dongsu Han ,  Hyunho Yeo ,  Youngmok Jung ,  Jaehong Kim ,  Jinwoo Shin

IPC分类号： H04N21/2383 ,  G06N3/04 ,  G06N3/08 ,  H04L67/61

摘要： A method and apparatus for transmitting adaptive video in real time using a content-aware neural network are disclosed. At least one embodiment provides a method performed by a server for transmitting an adaptive video in real time by using content-aware deep neural networks (DNNs), including downloading a video, encoding a downloaded video for each of at least one resolution, dividing an encoded video into video chunks of a predetermined size, training the content-aware DNNs by using encoded video, generating a configuration or manifest file containing information on trained content-aware DNNs and information on the encoded video, and transmitting the configuration file upon a request of a client.

公开(公告)号：US20180129930A1

公开(公告)日：2018-05-10

申请号：US15365641

申请日：2016-11-30

申请人： Korea Advanced Institute of Science and Technology

发明人： Jinwoo Shin ,  Song Chong ,  Kimin Lee

IPC分类号： G06N3/04 ,  G06N3/08

CPC分类号： G06N3/04 ,  G06N3/0472 ,  G06N3/08 ,  G06N3/084

摘要： Disclosed is a learning method based on a stochastic-based deep learning model having a non-consecutive stochastic neural. The learning method includes configuring a non-consecutive stochastic feedforward neural network (NCSFNN) having non-consecutive stochastic neuron as a leaning model including a plurality of hidden layers; and allowing the NCSFNN to learn.

公开(公告)号：US20200160212A1

公开(公告)日：2020-05-21

申请号：US16214598

申请日：2018-12-10

申请人： Korea Advanced Institute of Science and Technology

发明人： Jinwoo Shin ,  Sung Ju Hwang ,  Yunhun Jang

IPC分类号： G06N20/00

摘要： Disclosed are a method and system for transfer learning to a random target dataset and model structure based on meta learning. A transfer learning method may include determining the form and amount of information to be transferred, used by a pre-trained model, using a meta model based on similarity between a source dataset and a new target dataset and performing transfer-learning on a target model using the form and amount of information of the pre-trained model determined by the meta model.