PL3631697T3 - Sposoby i aparat do dyskryminacyjnego transferu semantycznego i inspirowanej fizyką optymalizacji cech w uczeniu głębokim - Google Patents
Sposoby i aparat do dyskryminacyjnego transferu semantycznego i inspirowanej fizyką optymalizacji cech w uczeniu głębokimInfo
- Publication number
- PL3631697T3 PL3631697T3 PL18805872.1T PL18805872T PL3631697T3 PL 3631697 T3 PL3631697 T3 PL 3631697T3 PL 18805872 T PL18805872 T PL 18805872T PL 3631697 T3 PL3631697 T3 PL 3631697T3
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- physics
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- deep learning
- feature optimization
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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
- G06N3/06—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
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
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- 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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- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
- G06F18/24133—Distances to prototypes
- G06F18/24143—Distances to neighbourhood prototypes, e.g. restricted Coulomb energy networks [RCEN]
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- G06V10/443—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by matching or filtering
- G06V10/449—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters
- G06V10/451—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters with interaction between the filter responses, e.g. cortical complex cells
- G06V10/454—Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]
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- G06V10/774—Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
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- G06V10/955—Hardware or software architectures specially adapted for image or video understanding using specific electronic processors
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- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
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- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/70—Labelling scene content, e.g. deriving syntactic or semantic representations
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- G—PHYSICS
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- 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
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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
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- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/047—Probabilistic or stochastic networks
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
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- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
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- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Software Systems (AREA)
- Multimedia (AREA)
- General Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Biomedical Technology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Biophysics (AREA)
- General Engineering & Computer Science (AREA)
- Molecular Biology (AREA)
- Mathematical Physics (AREA)
- Databases & Information Systems (AREA)
- Medical Informatics (AREA)
- Biodiversity & Conservation Biology (AREA)
- Neurology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Image Processing (AREA)
- Image Generation (AREA)
- Image Analysis (AREA)
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US201762509960P | 2017-05-23 | 2017-05-23 | |
| US201762509990P | 2017-05-23 | 2017-05-23 | |
| PCT/US2018/033986 WO2018217828A1 (en) | 2017-05-23 | 2018-05-22 | Methods and apparatus for discriminative semantic transfer and physics-inspired optimization of features in deep learning |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| PL3631697T3 true PL3631697T3 (pl) | 2025-09-15 |
Family
ID=64395967
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| PL18805872.1T PL3631697T3 (pl) | 2017-05-23 | 2018-05-22 | Sposoby i aparat do dyskryminacyjnego transferu semantycznego i inspirowanej fizyką optymalizacji cech w uczeniu głębokim |
Country Status (6)
| Country | Link |
|---|---|
| US (4) | US11669718B2 (pl) |
| EP (2) | EP4446941A3 (pl) |
| CN (2) | CN110892424A (pl) |
| ES (1) | ES3035808T3 (pl) |
| PL (1) | PL3631697T3 (pl) |
| WO (1) | WO2018217828A1 (pl) |
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| CN116843893A (zh) * | 2023-03-30 | 2023-10-03 | 北京工商大学 | 一种基于注意力机制多尺度卷积神经网络的三维图像分割方法及系统 |
| CN116109753B (zh) * | 2023-04-12 | 2023-06-23 | 深圳原世界科技有限公司 | 三维云渲染引擎装置及数据处理方法 |
| CN117391187B (zh) * | 2023-10-27 | 2024-06-25 | 广州恒沙数字科技有限公司 | 基于动态层次化掩码的神经网络有损传输优化方法及系统 |
| CN117237559B (zh) * | 2023-11-10 | 2024-02-27 | 陕西天润科技股份有限公司 | 面向数字孪生城市的三维模型数据智能分析方法及系统 |
| CN120744856B (zh) * | 2025-09-05 | 2025-11-18 | 浙江大学城乡规划设计研究院有限公司 | 一种面向城市测度的空地一体大数据融合方法 |
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| US9965705B2 (en) * | 2015-11-03 | 2018-05-08 | Baidu Usa Llc | Systems and methods for attention-based configurable convolutional neural networks (ABC-CNN) for visual question answering |
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| CN110892424A (zh) * | 2017-05-23 | 2020-03-17 | 英特尔公司 | 用于深度学习中的特征的区分性语义转移和物理启发优化的方法和装置 |
| US10540757B1 (en) * | 2018-03-12 | 2020-01-21 | Amazon Technologies, Inc. | Method and system for generating combined images utilizing image processing of multiple images |
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