SG10201802739PA - Neural network systems - Google Patents
Neural network systemsInfo
- Publication number
- SG10201802739PA SG10201802739PA SG10201802739PA SG10201802739PA SG10201802739PA SG 10201802739P A SG10201802739P A SG 10201802739PA SG 10201802739P A SG10201802739P A SG 10201802739PA SG 10201802739P A SG10201802739P A SG 10201802739PA SG 10201802739P A SG10201802739P A SG 10201802739PA
- Authority
- SG
- Singapore
- Prior art keywords
- network
- artificial neural
- systems
- task images
- neural network
- Prior art date
Links
Classifications
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- 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
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- 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
- 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
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- 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
-
- 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
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
- G06V20/64—Three-dimensional objects
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Biophysics (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Computing Systems (AREA)
- Computational Linguistics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Molecular Biology (AREA)
- Multimedia (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Neurology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Human Computer Interaction (AREA)
- Image Analysis (AREA)
- Image Processing (AREA)
Abstract
NEURAL NETWORK SYSTEMS Systems and methods are provided relating to artificial neural networks are provided. The systems and methods obtain a teacher network that includes artificial neural layers configured to automatically identify one or more objects in an image examined by the artificial neural layers, receive a set of task images at the teacher network, examine the set of task images with the teacher network, identify a subset of the artificial neural layers that are utilized during examination of the set of task images with the teacher network, and define a student network based on the set of task images. The student network is configured to automatically identify one or more objects in an image examined by the subset. [Figure 1]
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US15/493,765 US10592725B2 (en) | 2017-04-21 | 2017-04-21 | Neural network systems |
Publications (1)
Publication Number | Publication Date |
---|---|
SG10201802739PA true SG10201802739PA (en) | 2018-11-29 |
Family
ID=62046664
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
SG10201802739PA SG10201802739PA (en) | 2017-04-21 | 2018-04-02 | Neural network systems |
Country Status (5)
Country | Link |
---|---|
US (1) | US10592725B2 (en) |
EP (1) | EP3392806A1 (en) |
CN (1) | CN108734283B (en) |
CA (1) | CA3001193A1 (en) |
SG (1) | SG10201802739PA (en) |
Families Citing this family (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US10497084B2 (en) | 2017-04-24 | 2019-12-03 | Intel Corporation | Efficient sharing and compression expansion of data across processing systems |
US11216437B2 (en) | 2017-08-14 | 2022-01-04 | Sisense Ltd. | System and method for representing query elements in an artificial neural network |
WO2020072205A1 (en) * | 2018-10-01 | 2020-04-09 | Google Llc | Systems and methods for providing a machine-learned model with adjustable computational demand |
CN111179212B (en) * | 2018-11-10 | 2023-05-23 | 杭州凝眸智能科技有限公司 | Method for realizing tiny target detection on-chip by integrating distillation strategy and deconvolution |
RU2697613C9 (en) * | 2018-11-20 | 2022-04-15 | Хуавей Текнолоджис Ко., Лтд. | Method of objects recognition using neural networks |
US20200226458A1 (en) * | 2019-01-10 | 2020-07-16 | Mipsology SAS | Optimizing artificial neural network computations based on automatic determination of a batch size |
CN109886343B (en) * | 2019-02-26 | 2024-01-05 | 深圳市商汤科技有限公司 | Image classification method and device, equipment and storage medium |
CN110097084B (en) * | 2019-04-03 | 2021-08-31 | 浙江大学 | Knowledge fusion method for training multitask student network through projection characteristics |
CN110009052B (en) * | 2019-04-11 | 2022-11-18 | 腾讯科技(深圳)有限公司 | Image recognition method, image recognition model training method and device |
CN111291836B (en) * | 2020-03-31 | 2023-09-08 | 中国科学院计算技术研究所 | Method for generating student network model |
CN113869464B (en) * | 2021-12-02 | 2022-03-18 | 深圳佑驾创新科技有限公司 | Training method of image classification model and image classification method |
CN117011617B (en) * | 2023-10-07 | 2024-03-22 | 之江实验室 | Pulmonary nodule detection device based on two-stage teacher-student framework and construction method |
Family Cites Families (24)
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US5845271A (en) * | 1996-01-26 | 1998-12-01 | Thaler; Stephen L. | Non-algorithmically implemented artificial neural networks and components thereof |
US20030118230A1 (en) * | 2001-12-22 | 2003-06-26 | Haoshi Song | Coiled tubing inspection system using image pattern recognition |
DE102005010076A1 (en) * | 2005-03-04 | 2006-09-07 | Siemens Ag | Image processing method for a digital medical examination image and associated examination device |
US7785180B1 (en) | 2005-07-15 | 2010-08-31 | Carnegie Mellon University | Method, apparatus, and system for object recognition, object segmentation and knowledge acquisition |
US20080281868A1 (en) | 2007-02-26 | 2008-11-13 | Connections Center | Methods, apparatus and products for transferring knowledge |
US8391603B2 (en) | 2009-06-18 | 2013-03-05 | Omisa Inc. | System and method for image segmentation |
WO2013020143A1 (en) * | 2011-08-04 | 2013-02-07 | University Of Southern California | Image-based crack quantification |
US20140025613A1 (en) | 2012-07-20 | 2014-01-23 | Filip Ponulak | Apparatus and methods for reinforcement learning in large populations of artificial spiking neurons |
JP5921990B2 (en) | 2012-08-23 | 2016-05-24 | 株式会社ニューフレアテクノロジー | Defect detection method |
US9008840B1 (en) | 2013-04-19 | 2015-04-14 | Brain Corporation | Apparatus and methods for reinforcement-guided supervised learning |
US9189968B2 (en) * | 2013-07-01 | 2015-11-17 | Pearson Education, Inc. | Network-probability recommendation system |
US20150019468A1 (en) | 2013-07-09 | 2015-01-15 | Knowmtech, Llc | Thermodynamic computing |
US10417525B2 (en) * | 2014-09-22 | 2019-09-17 | Samsung Electronics Co., Ltd. | Object recognition with reduced neural network weight precision |
US20160132787A1 (en) | 2014-11-11 | 2016-05-12 | Massachusetts Institute Of Technology | Distributed, multi-model, self-learning platform for machine learning |
KR20170113619A (en) * | 2015-02-06 | 2017-10-12 | 센스 에듀케이션 이스라엘., 엘티디. | Semi-automated systems and methods for evaluating responses |
US10373054B2 (en) | 2015-04-19 | 2019-08-06 | International Business Machines Corporation | Annealed dropout training of neural networks |
US10438112B2 (en) * | 2015-05-26 | 2019-10-08 | Samsung Electronics Co., Ltd. | Method and apparatus of learning neural network via hierarchical ensemble learning |
US11423311B2 (en) | 2015-06-04 | 2022-08-23 | Samsung Electronics Co., Ltd. | Automatic tuning of artificial neural networks |
US9978374B2 (en) * | 2015-09-04 | 2018-05-22 | Google Llc | Neural networks for speaker verification |
CN105681920B (en) * | 2015-12-30 | 2017-03-15 | 深圳市鹰硕音频科技有限公司 | A kind of Network teaching method and system with speech identifying function |
US20170206434A1 (en) * | 2016-01-14 | 2017-07-20 | Ford Global Technologies, Llc | Low- and high-fidelity classifiers applied to road-scene images |
US9753949B1 (en) * | 2016-03-14 | 2017-09-05 | Shutterstock, Inc. | Region-specific image download probability modeling |
US10891541B2 (en) * | 2016-05-16 | 2021-01-12 | Canon Kabushiki Kaisha | Devices, systems, and methods for feature encoding |
US10510146B2 (en) * | 2016-10-06 | 2019-12-17 | Qualcomm Incorporated | Neural network for image processing |
-
2017
- 2017-04-21 US US15/493,765 patent/US10592725B2/en active Active
-
2018
- 2018-04-02 SG SG10201802739PA patent/SG10201802739PA/en unknown
- 2018-04-12 CA CA3001193A patent/CA3001193A1/en not_active Abandoned
- 2018-04-17 EP EP18167803.8A patent/EP3392806A1/en not_active Ceased
- 2018-04-20 CN CN201810364434.8A patent/CN108734283B/en active Active
Also Published As
Publication number | Publication date |
---|---|
CN108734283B (en) | 2023-06-09 |
US10592725B2 (en) | 2020-03-17 |
CN108734283A (en) | 2018-11-02 |
US20180307894A1 (en) | 2018-10-25 |
CA3001193A1 (en) | 2018-10-21 |
EP3392806A1 (en) | 2018-10-24 |
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