DE112018002051A5 - Wissenstransfer zwischen verschiedenen Deep-Learning Architekturen - Google Patents

Wissenstransfer zwischen verschiedenen Deep-Learning Architekturen Download PDF

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Publication number
DE112018002051A5
DE112018002051A5 DE112018002051.7T DE112018002051T DE112018002051A5 DE 112018002051 A5 DE112018002051 A5 DE 112018002051A5 DE 112018002051 T DE112018002051 T DE 112018002051T DE 112018002051 A5 DE112018002051 A5 DE 112018002051A5
Authority
DE
Germany
Prior art keywords
deep learning
knowledge transfer
different deep
learning architectures
architectures
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
DE112018002051.7T
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English (en)
Inventor
Michelle Karg
Christian Scharfenberger
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Continental Autonomous Mobility Germany GmbH
Original Assignee
Conti Temic Microelectronic GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Conti Temic Microelectronic GmbH filed Critical Conti Temic Microelectronic GmbH
Publication of DE112018002051A5 publication Critical patent/DE112018002051A5/de
Pending legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0002—Automatic control, details of type of controller or control system architecture
    • B60W2050/0018—Method for the design of a control system
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
    • G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/417—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section involving the use of neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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
    • G06N3/044—Recurrent networks, e.g. Hopfield networks

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Data Mining & Analysis (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Computing Systems (AREA)
  • Molecular Biology (AREA)
  • General Health & Medical Sciences (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Computational Linguistics (AREA)
  • Neurology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Biology (AREA)
  • Image Analysis (AREA)
  • Traffic Control Systems (AREA)
DE112018002051.7T 2017-06-30 2018-06-28 Wissenstransfer zwischen verschiedenen Deep-Learning Architekturen Pending DE112018002051A5 (de)

Applications Claiming Priority (5)

Application Number Priority Date Filing Date Title
DE102017211199.2 2017-06-30
DE102017211199 2017-06-30
DE102017213247.7 2017-08-01
DE102017213247.7A DE102017213247A1 (de) 2017-06-30 2017-08-01 Wissenstransfer zwischen verschiedenen Deep-Learning Architekturen
PCT/DE2018/200065 WO2019001649A1 (de) 2017-06-30 2018-06-28 Wissenstransfer zwischen verschiedenen deep-learning architekturen

Publications (1)

Publication Number Publication Date
DE112018002051A5 true DE112018002051A5 (de) 2019-12-24

Family

ID=64662020

Family Applications (2)

Application Number Title Priority Date Filing Date
DE102017213247.7A Withdrawn DE102017213247A1 (de) 2017-06-30 2017-08-01 Wissenstransfer zwischen verschiedenen Deep-Learning Architekturen
DE112018002051.7T Pending DE112018002051A5 (de) 2017-06-30 2018-06-28 Wissenstransfer zwischen verschiedenen Deep-Learning Architekturen

Family Applications Before (1)

Application Number Title Priority Date Filing Date
DE102017213247.7A Withdrawn DE102017213247A1 (de) 2017-06-30 2017-08-01 Wissenstransfer zwischen verschiedenen Deep-Learning Architekturen

Country Status (4)

Country Link
US (1) US20210056388A1 (de)
CN (1) CN110799996A (de)
DE (2) DE102017213247A1 (de)
WO (1) WO2019001649A1 (de)

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US11501111B2 (en) 2018-04-06 2022-11-15 International Business Machines Corporation Learning models for entity resolution using active learning
DE102019204136A1 (de) * 2019-03-26 2020-10-01 Robert Bosch Gmbh Verfahren und Vorrichtung für Training und Herstellung eines künstlichen neuronalen Netzes
US11875253B2 (en) * 2019-06-17 2024-01-16 International Business Machines Corporation Low-resource entity resolution with transfer learning
DE102019209976A1 (de) * 2019-07-08 2021-01-14 Krones Ag Verfahren und Vorrichtung zur Inspektion von Behältern in einem Behältermassenstrom
US11783187B2 (en) 2020-03-04 2023-10-10 Here Global B.V. Method, apparatus, and system for progressive training of evolving machine learning architectures
EP3929554A1 (de) * 2020-06-26 2021-12-29 Siemens Aktiengesellschaft Verbesserte fehlererkennung bei maschinen mittels ki
DE102020118504A1 (de) 2020-07-14 2022-01-20 Bayerische Motoren Werke Aktiengesellschaft Verfahren und System zum Erweitern eines neuronalen Netzes für eine Umfelderkennung
DE102020006267A1 (de) * 2020-10-12 2022-04-14 Daimler Ag Verfahren zum Erzeugen eines Verhaltensmodells für eine Kraftfahrzeugflotte mittels einer kraftfahrzeugexternen elektronischen Recheneinrichtung, sowie kraftfahrzeugexterne elektronische Recheneinrichtung
CN116010804B (zh) * 2023-02-01 2023-07-04 南京邮电大学 基于深度学习与知识迁移的物联网设备小样本识别方法

Family Cites Families (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102004030782A1 (de) * 2004-06-25 2006-01-19 Fev Motorentechnik Gmbh Fahrzeug-Steuergerät mit einem neuronalen Netz
US9235799B2 (en) * 2011-11-26 2016-01-12 Microsoft Technology Licensing, Llc Discriminative pretraining of deep neural networks
JP6042274B2 (ja) * 2013-06-28 2016-12-14 株式会社デンソーアイティーラボラトリ ニューラルネットワーク最適化方法、ニューラルネットワーク最適化装置及びプログラム
US10223635B2 (en) * 2015-01-22 2019-03-05 Qualcomm Incorporated Model compression and fine-tuning
EP3845427A1 (de) * 2015-02-10 2021-07-07 Mobileye Vision Technologies Ltd. Spärliche karte für autonome fahrzeugnavigation
US9864932B2 (en) * 2015-04-14 2018-01-09 Conduent Business Services, Llc Vision-based object detector
CN106203506B (zh) * 2016-07-11 2019-06-21 上海凌科智能科技有限公司 一种基于深度学习技术的行人检测方法
CN106322656B (zh) * 2016-08-23 2019-05-14 海信（山东）空调有限公司 一种空调控制方法及服务器和空调系统
CN106355248A (zh) * 2016-08-26 2017-01-25 深圳先进技术研究院 一种深度卷积神经网络训练方法及装置
KR102415506B1 (ko) * 2016-10-26 2022-07-01 삼성전자주식회사 뉴럴 네트워크 간소화 방법 및 장치

Also Published As

Publication number Publication date
CN110799996A (zh) 2020-02-14
WO2019001649A1 (de) 2019-01-03
DE102017213247A1 (de) 2019-01-03
US20210056388A1 (en) 2021-02-25

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Legal Events

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R081 Change of applicant/patentee

Owner name: CONTINENTAL AUTONOMOUS MOBILITY GERMANY GMBH, DE

Free format text: FORMER OWNER: CONTI TEMIC MICROELECTRONIC GMBH, 90411 NUERNBERG, DE

R083 Amendment of/additions to inventor(s)
R081 Change of applicant/patentee

Owner name: CONTINENTAL AUTONOMOUS MOBILITY GERMANY GMBH, DE

Free format text: FORMER OWNER: CONTI TEMIC MICROELECTRONIC GMBH, 90411 NUERNBERG, DE