Zhao et al., 2021 - Google Patents
Multi-Objective Net Architecture Pruning for Remote Sensing ClassificationZhao et al., 2021
- Document ID
- 17577048403646455914
- Author
- Zhao J
- Yang C
- Zhou Y
- Zhou Y
- Jiang Z
- Chen Y
- Publication year
- Publication venue
- 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
External Links
Snippet
Remote sensing image scene classification has achieved significant breakthroughs in recent years. However, due to the high complexity and expensive computation most of CNNs used in the field of remote sensing imagery scene classification, it has become a …
- 238000005457 optimization 0 abstract description 24
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30386—Retrieval requests
- G06F17/30424—Query processing
- G06F17/30533—Other types of queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/08—Learning methods
- G06N3/082—Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/04—Architectures, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6251—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on a criterion of topology preservation, e.g. multidimensional scaling, self-organising maps
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Song et al. | Efficient residual dense block search for image super-resolution | |
Yu et al. | Auto-fas: Searching lightweight networks for face anti-spoofing | |
CN114037891A (en) | High-resolution remote sensing image building extraction method and device based on U-shaped attention control network | |
CN114419464A (en) | Twin network change detection model based on deep learning | |
Johner et al. | Efficient evolutionary architecture search for CNN optimization on GTSRB | |
Zhao et al. | Multi-Objective Net Architecture Pruning for Remote Sensing Classification | |
CN116777745A (en) | Image super-resolution reconstruction method based on sparse self-adaptive clustering | |
Chang et al. | A Triple-Branch Hybrid Attention Network With Bitemporal Feature Joint Refinement For Remote Sensing Image Semantic Change Detection | |
Yin et al. | Research on remote sensing image classification algorithm based on EfficientNet | |
Zhang et al. | Particle swarm optimization based deep learning architecture search for hyperspectral image classification | |
CN114898171A (en) | Real-time target detection method suitable for embedded platform | |
CN112766134A (en) | Expression recognition method for enhancing class distinction | |
Liu et al. | Real-time object detection in UAV vision based on neural processing units | |
Liu et al. | Fault diagnosis of chillers using very deep convolutional network | |
ZiWen et al. | Multi-objective Neural Architecture Search for Efficient and Fast Semantic Segmentation on Edge | |
CN113159405B (en) | Wind power prediction method for optimizing LSSVR (least Square support vector regression) based on improved satin blue gardener algorithm | |
Jing et al. | NASABN: A neural architecture search framework for attention-based networks | |
CN111612127B (en) | Multi-direction information propagation convolution neural network construction method for hyperspectral image classification | |
Niu et al. | Neural architecture search based on particle swarm optimization | |
CN115292509A (en) | Graph cube link prediction method based on multi-granularity attention network | |
Han et al. | Detection of Face Mask Wearing for COVID-19 Protection based on Transfer Learning and Classic CNN Model | |
CN118330787B (en) | Typhoon generation forecasting method based on multi-modal domain transformation and adaptive fusion | |
CN117953296B (en) | Neural network architecture searching method for remote sensing image classification | |
Ding et al. | A Novel Performance Evaluation Strategy of Automatic Machine Learning on Electricity Services | |
CN118196600B (en) | Neural architecture searching method and system based on differential evolution algorithm |