JP7575388B2 - 低変位ランクベースのディープニューラルネットワーク圧縮 - Google Patents
低変位ランクベースのディープニューラルネットワーク圧縮 Download PDFInfo
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- JP7575388B2 JP7575388B2 JP2021548231A JP2021548231A JP7575388B2 JP 7575388 B2 JP7575388 B2 JP 7575388B2 JP 2021548231 A JP2021548231 A JP 2021548231A JP 2021548231 A JP2021548231 A JP 2021548231A JP 7575388 B2 JP7575388 B2 JP 7575388B2
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/082—Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0495—Quantised networks; Sparse networks; Compressed networks
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/42—Methods 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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Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US201962818914P | 2019-03-15 | 2019-03-15 | |
| US62/818,914 | 2019-03-15 | ||
| PCT/US2020/022585 WO2020190696A1 (en) | 2019-03-15 | 2020-03-13 | Low displacement rank based deep neural network compression |
Publications (3)
| Publication Number | Publication Date |
|---|---|
| JP2022525392A JP2022525392A (ja) | 2022-05-13 |
| JP2022525392A5 JP2022525392A5 (enExample) | 2023-03-13 |
| JP7575388B2 true JP7575388B2 (ja) | 2024-10-29 |
Family
ID=70228824
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP2021548231A Active JP7575388B2 (ja) | 2019-03-15 | 2020-03-13 | 低変位ランクベースのディープニューラルネットワーク圧縮 |
Country Status (6)
| Country | Link |
|---|---|
| US (1) | US20220188633A1 (enExample) |
| EP (1) | EP3939301A1 (enExample) |
| JP (1) | JP7575388B2 (enExample) |
| CN (1) | CN113574887B (enExample) |
| MX (1) | MX2021011131A (enExample) |
| WO (1) | WO2020190696A1 (enExample) |
Families Citing this family (6)
| 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 (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016199330A1 (ja) | 2015-06-12 | 2016-12-15 | パナソニックIpマネジメント株式会社 | 画像符号化方法、画像復号方法、画像符号化装置および画像復号装置 |
| US20180239992A1 (en) | 2017-02-22 | 2018-08-23 | Arm Limited | Processing artificial neural network weights |
| WO2019008752A1 (ja) | 2017-07-07 | 2019-01-10 | 三菱電機株式会社 | データ処理装置、データ処理方法および圧縮データ |
Family Cites Families (14)
| 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 |
| US10515307B2 (en) * | 2015-06-05 | 2019-12-24 | Google Llc | Compressed recurrent neural network models |
| 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 |
| US11037330B2 (en) * | 2017-04-08 | 2021-06-15 | Intel Corporation | Low rank matrix compression |
| 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 |
-
2020
- 2020-03-13 MX MX2021011131A patent/MX2021011131A/es unknown
- 2020-03-13 JP JP2021548231A patent/JP7575388B2/ja active Active
- 2020-03-13 WO PCT/US2020/022585 patent/WO2020190696A1/en not_active Ceased
- 2020-03-13 US US17/438,079 patent/US20220188633A1/en active Pending
- 2020-03-13 EP EP20718043.1A patent/EP3939301A1/en active Pending
- 2020-03-13 CN CN202080021701.7A patent/CN113574887B/zh active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016199330A1 (ja) | 2015-06-12 | 2016-12-15 | パナソニックIpマネジメント株式会社 | 画像符号化方法、画像復号方法、画像符号化装置および画像復号装置 |
| US20180239992A1 (en) | 2017-02-22 | 2018-08-23 | Arm Limited | Processing artificial neural network weights |
| WO2019008752A1 (ja) | 2017-07-07 | 2019-01-10 | 三菱電機株式会社 | データ処理装置、データ処理方法および圧縮データ |
Non-Patent Citations (7)
| Title |
|---|
| "Use cases and requirements for compressed representation of neural networks",N17740,[online], ISO/IEC JTC1/SC29/WG11,2018年07月20日,Pages 1-4,[令和6年1月15日検索], インターネット, <URL: https://www.mpeg.org/wp-content/uploads/mpeg_meetings/123_Ljubljana/w17740.zip> and <URL: https://www.mpeg.org/standards/Explorations/29/>.,(See document file "w17740.docx" in the zip file "w17740.zip".) |
| Anna T. Thomas, et al.,"Learning Compressed Transforms with Low Displacement Rank",arXiv:1810.02309v3,version v3,[online], arXiv (Cornell University),2019年01月01日,Pages 1-33,[令和6年6月7日検索], インターネット, <URL: https://arxiv.org/abs/1810.02309v3> and <URL: https://arxiv.org/pdf/1810.02309v3>. |
| Liang Zhao, et al.,"Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank",arXiv:1703.00144v4,version v4,[online], arXiv (Cornell University),2017年09月22日,Pages 1-13,[令和6年1月14日検索], インターネット, <URL: https://arxiv.org/abs/1703.00144v4> and <URL: https://arxiv.org/pdf/1703.00144v4.pdf>. |
| VICTOR Y. PAN, et al.,"INVERSION OF DISPLACEMENT OPERATORS",SIAM J. MATRIX ANAL. APPL.,Vol.24, No.3,[online], Society for Industrial and Applied Mathematics,2003年01月23日,Pages 660-677,[令和6年1月14日検索], インターネット, <URL: http://comet.lehman.cuny.edu/vpan/pdf/38627.pdf>. |
| Zhiyun Lu, et al.,"LEARNING COMPACT RECURRENT NEURAL NETWORKS",Proceedings of 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2016),IEEE,2016年03月25日,Pages 5960-5964,ISBN: 978-1-4799-9988-0, <DOI: 10.1109/ICASSP.2016.7472821>. |
| 武井 宏将,「初めてのディープラーニング オープンソース"Caffe"による演習付き」,第1版,日本,株式会社リックテレコム,2016年03月04日,第111~115,123頁,ISBN: 978-4-86594-022-0. |
| 長尾 真(外7名)編,「岩波情報科学辞典」,第1刷,日本,株式会社岩波書店,1990年05月25日,第590頁,ISBN: 4-00-080074-4. |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113574887A (zh) | 2021-10-29 |
| US20220188633A1 (en) | 2022-06-16 |
| CN113574887B (zh) | 2024-09-27 |
| MX2021011131A (es) | 2021-10-14 |
| JP2022525392A (ja) | 2022-05-13 |
| WO2020190696A1 (en) | 2020-09-24 |
| EP3939301A1 (en) | 2022-01-19 |
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