CN113574887B - 基于低位移秩的深度神经网络压缩 - Google Patents

基于低位移秩的深度神经网络压缩 Download PDF

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CN113574887B
CN113574887B CN202080021701.7A CN202080021701A CN113574887B CN 113574887 B CN113574887 B CN 113574887B CN 202080021701 A CN202080021701 A CN 202080021701A CN 113574887 B CN113574887 B CN 113574887B
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CN113574887A (zh
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F·拉卡佩
S·杰恩
S·哈米迪拉德
D·帕帕季米特里乌
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InterDigital VC Holdings Inc
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    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/08Learning methods
    • G06N3/082Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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    • G06N3/08Learning methods
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/04Architecture, e.g. interconnection topology
    • G06N3/0495Quantised networks; Sparse networks; Compressed networks
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04NPICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/42Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation

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CN202080021701.7A 2019-03-15 2020-03-13 基于低位移秩的深度神经网络压缩 Active CN113574887B (zh)

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US201962818914P 2019-03-15 2019-03-15
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PCT/US2020/022585 WO2020190696A1 (en) 2019-03-15 2020-03-13 Low displacement rank based deep neural network compression

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JP (1) JP7575388B2 (enExample)
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Families Citing this family (6)

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Publication number Priority date Publication date Assignee Title
US11037330B2 (en) * 2017-04-08 2021-06-15 Intel Corporation Low rank matrix compression
US11700518B2 (en) * 2019-05-31 2023-07-11 Huawei Technologies Co., Ltd. Methods and systems for relaying feature-driven communications
US20210326710A1 (en) * 2020-04-16 2021-10-21 Tencent America LLC Neural network model compression
CN114698394A (zh) * 2020-10-29 2022-07-01 华为技术有限公司 一种基于神经网络模型的量化方法及其相关设备
US11818399B2 (en) * 2021-01-04 2023-11-14 Tencent America LLC Techniques for signaling neural network topology and parameters in the coded video stream
CN112836801A (zh) * 2021-02-03 2021-05-25 上海商汤智能科技有限公司 深度学习网络确定方法、装置、电子设备及存储介质

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107145940A (zh) * 2016-03-01 2017-09-08 谷歌公司 压缩的递归神经网络模型

Family Cites Families (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR100850729B1 (ko) * 2000-07-06 2008-08-06 더 트러스티스 오브 콜롬비아 유니버시티 인 더 시티 오브 뉴욕 데이터 해상도를 향상시키는 방법 및 장치
US7133568B2 (en) * 2000-08-04 2006-11-07 Nikitin Alexei V Method and apparatus for analysis of variables
CN107736027B (zh) * 2015-06-12 2021-06-01 松下知识产权经营株式会社 图像编码方法、图像解码方法、图像编码装置及图像解码装置
US11321609B2 (en) * 2016-10-19 2022-05-03 Samsung Electronics Co., Ltd Method and apparatus for neural network quantization
US12079700B2 (en) * 2016-10-26 2024-09-03 Google Llc Structured orthogonal random features for kernel-based machine learning
US10599935B2 (en) 2017-02-22 2020-03-24 Arm Limited Processing artificial neural network weights
US11037330B2 (en) * 2017-04-08 2021-06-15 Intel Corporation Low rank matrix compression
BR112019027664B1 (pt) 2017-07-07 2023-12-19 Mitsubishi Electric Corporation Dispositivo e método de processamento de dados, e, meio de armazenamento
JP6789894B2 (ja) * 2017-07-31 2020-11-25 株式会社東芝 ネットワーク係数圧縮装置、ネットワーク係数圧縮方法およびプログラム
EP3451293A1 (en) * 2017-08-28 2019-03-06 Thomson Licensing Method and apparatus for filtering with multi-branch deep learning
CN107396124B (zh) * 2017-08-29 2019-09-20 南京大学 基于深度神经网络的视频压缩方法
EP3704638A1 (en) * 2017-10-30 2020-09-09 Fraunhofer Gesellschaft zur Förderung der Angewand Neural network representation
US11423259B1 (en) * 2017-12-12 2022-08-23 Amazon Technologies, Inc. Trained model approximation
WO2019115865A1 (en) * 2017-12-13 2019-06-20 Nokia Technologies Oy An apparatus, a method and a computer program for video coding and decoding
US11429849B2 (en) * 2018-05-11 2022-08-30 Intel Corporation Deep compressed network
KR20200115239A (ko) * 2019-03-26 2020-10-07 (주)인시그널 훈련된 심층 신경망의 압축 장치 및 방법

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107145940A (zh) * 2016-03-01 2017-09-08 谷歌公司 压缩的递归神经网络模型

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank;Liang Zhao ET AL;《arXiv》;20170331;第1-13页 *

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CN113574887A (zh) 2021-10-29
US20220188633A1 (en) 2022-06-16
JP7575388B2 (ja) 2024-10-29
MX2021011131A (es) 2021-10-14
JP2022525392A (ja) 2022-05-13
WO2020190696A1 (en) 2020-09-24
EP3939301A1 (en) 2022-01-19

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