Suchergebnisse - Multi-layer Conventional Sparse Coding

Interpretable convolutional sparse coding method of Lamb waves for damage identification and localization

Autoren: Han Zhang Jing Lin Jiadong Hua et al.

Quelle: Structural Health Monitoring. 21:1790-1804

Schlagwörter: 0103 physical sciences, 01 natural sciences

Zugangs-URL: https://journals.sagepub.com/doi/abs/10.1177/14759217211044806

Multi Attention Convolutional Sparse Coding U-Net for Enhanced Land-Use and Land-Cover Segmentation Using Hyperspectral Images.

Autoren: Yele, Vijaykumar P. Badhe, Namdeo Baban Alegavi, Sujata et al.

Quelle: Sensing & Imaging; 5/20/2025, Vol. 26 Issue 1, p1-32, 32p

Learning the sparse prior: Modern approaches.

Autoren: Peng, Guan‐Ju¹ (AUTHOR) gjpeng@email.nchu.edu.tw

Quelle: WIREs: Computational Statistics. Jan/Feb2024, Vol. 16 Issue 1, p1-29. 29p.

Schlagwörter: *RECURRENT neural networks, *SUPERVISED learning, *ENCYCLOPEDIAS & dictionaries, *PRIOR learning, *DATA science

Learned Multi-layer Residual Sparsifying Transform Model for Low-dose CT Reconstruction

Autoren: Yang, Xikai Zheng, Xuehang Long, Yong et al.

Index Begriffe: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Machine Learning, Statistics - Machine Learning, text

URL: http://arxiv.org/abs/2005.03825

Supervised Deep Sparse Coding Networks for Image Classification.

Autoren: Sun, Xiaoxia Nasrabadi, Nasser M. Tran, Trac D.

Quelle: IEEE Transactions on Image Processing; 2020, Vol. 29, p405-418, 14p

Simple and complex cells revisited: toward a selectivity-invariance model of object recognition.

Autoren: Xin Li Shuo Wang

Quelle: Frontiers in Computational Neuroscience; 2023, p1-13, 13p

Schlagwörter: ARTIFICIAL neural networks, OBJECT recognition (Computer vision), TEMPORAL lobe, TOPOLOGICAL spaces

SUPER-RESOLUTION IMAGE RECONSTRUCTION METHOD BASED ON DEEP CONVOLUTIONAL SPARSE CODING

Deep Learning for Passive Synthetic Aperture Radar.

Autoren: Yonel, Bariscan Mason, Eric Yazici, Birsen

Quelle: IEEE Journal of Selected Topics in Signal Processing; Feb2018, Vol. 12 Issue 1, p90-103, 14p

Interpretable convolutional sparse coding method of Lamb waves for damage identification and localization.

Autoren: Zhang, Han Lin, Jing Hua, Jiadong et al.

Quelle: Structural Health Monitoring; Jul2022, Vol. 21 Issue 4, p1790-1804, 15p

Schlagwörter: LAMB waves, DEEP learning, STRUCTURAL health monitoring, FEATURE extraction, NONDESTRUCTIVE testing, CONVOLUTIONAL neural networks

Damage localization with Lamb waves using dense convolutional sparse coding network.

Autoren: Zhang, Han Hua, Jiadong Lin, Jing et al.

Quelle: Structural Health Monitoring; Mar2023, Vol. 22 Issue 2, p1180-1192, 13p

Schlagwörter: LAMB waves, DEEP learning, LINEAR network coding, MACHINE learning, STRUCTURAL health monitoring, THRESHOLDING algorithms, MATHEMATICAL convolutions

Locality constrained dictionary learning for non-linear dimensionality reduction and classification.

Autoren: Lina Liu Shiwei Ma Ling Rui et al.

Quelle: IET Computer Vision (Wiley-Blackwell); 2017, Vol. 11 Issue 1, p60-67, 8p

Schlagwörter: DIMENSIONAL reduction algorithms, MACHINE learning, MANIFOLDS (Mathematics), IMAGE reconstruction, COMPUTER vision

多媒体信号处理的数学理论前沿进展. (Chinese)

Alternate Title: Advances in mathematical theory for multimedia signal processing. (English)

Autoren: 熊红凯 戴文睿 林宙晨 et al.

Quelle: Journal of Image & Graphics; Jan2020, Vol. 25 Issue 1, p1-18, 18p

Schlagwörter: ARTIFICIAL neural networks, SIGNAL theory, SIGNAL processing, SPARSE graphs, NON-Euclidean geometry, MULTIMEDIA systems, HIERARCHICAL Bayes model, VIDEO coding, IMAGE representation

Inter-Frame Compression for Dynamic Point Cloud Geometry Coding.

Autoren: Akhtar, Anique Li, Zhu Van der Auwera, Geert

Quelle: IEEE Transactions on Image Processing; 2024, Vol. 33, p584-594, 11p

Remote sensing image super-resolution using multi-scale convolutional sparse coding network.

Autoren: Cheng, Ruihong Wang, Huajun Luo, Ping

Quelle: PLoS ONE; 10/26/2022, Vol. 17 Issue 10, p1-15, 15p

Schlagwörter: REMOTE sensing, HIGH resolution imaging, LINEAR network coding, CONVOLUTIONAL neural networks, IMAGE reconstruction

PBC-Transformer: Interpreting Poultry Behavior Classification Using Image Caption Generation Techniques.

Autoren: Li, Jun Yang, Bing Liu, Jiaxin et al.

Quelle: Animals (2076-2615); Jun2025, Vol. 15 Issue 11, p1546, 28p

Schlagwörter: ANIMAL welfare, IMAGE recognition (Computer vision), FEATURE extraction, TRANSFORMER models, ANIMAL health

Interleaving cortex-analog mixing improves deep non-negative matrix factorization networks.

Autoren: Nouri, Mahbod Rotermund, David Garcia-Ortiz, Alberto et al.

Quelle: Frontiers in Computational Neuroscience; 2025, p1-11, 11p

Schlagwörter: ARTIFICIAL neural networks, CONVOLUTIONAL neural networks, MATHEMATICAL optimization, NEURAL computers, DEEP learning

Sparse coding of pathology slides compared to transfer learning with deep neural networks.

Autoren: Fischer, Will Moudgalya, Sanketh S. Cohn, Judith D. et al.

Quelle: BMC Bioinformatics; 12/21/2018 Supplement 18, Vol. 19 Issue 18, p9-17, 9p, 4 Color Photographs, 2 Charts, 1 Graph

Schlagwörter: ARTIFICIAL neural networks, ARTIFICIAL intelligence, DEEP learning, CANCER treatment, CANCER cells

Machine Learning Models of the Geospatial Distribution of Groundwater Quality: A Systematic Review.

Autoren: Mehrabi, Mohammad Polya, David A. Han, Yang

Quelle: Water (20734441); Oct2025, Vol. 17 Issue 19, p2861, 48p

Schlagwörter: MACHINE learning, GROUNDWATER quality, STATISTICAL reliability, PREDICTION models, SPATIAL variation, ENVIRONMENTAL risk assessment, POLLUTANTS

Activated Sparsely Sub-Pixel Transformer for Remote Sensing Image Super-Resolution.

Autoren: Guo, Yongde Gong, Chengying Yan, Jun

Quelle: Remote Sensing; Jun2024, Vol. 16 Issue 11, p1895, 20p

Schlagwörter: TRANSFORMER models, HIGH resolution imaging, FEATURE selection, CODING theory

Optimization of Genomic Breeding Value Estimation Model for Abdominal Fat Traits Based on Machine Learning.

Autoren: Chen, Hengcong Dou, Dachang Lu, Min et al.

Quelle: Animals (2076-2615); Oct2025, Vol. 15 Issue 19, p2843, 24p

Schlagwörter: MACHINE learning, POULTRY breeding, ABDOMINAL adipose tissue, GENETIC risk score, GENETIC markers, FEATURE selection, FORECASTING, ENSEMBLE learning