Suchergebnisse - "multilayer convolutional sparse coding"

An Improved Multilayer Convolutional Sparse Coding Model for Fault Feature Extraction of Rolling Element Bearings Under Varying Speed Conditions

Autoren: Guangming Dong

Quelle: IEEE Transactions on Instrumentation and Measurement. 74:1-12

On Interpretability of CNNs for Multimodal Medical Image Segmentation

Autoren: Lazendic, Srdan Janssens, Jens Huang, Shaoguang et al.

Quelle: EUSIPCO 2022, 30th European Signal Processing Conference, Proceedings

Schlagwörter: multimodal data, medical image segmentation, 03 medical and health sciences, Technology and Engineering, 0302 clinical medicine, deep unrolling, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, interpretable CNNs, Multilayer convolutional sparse coding

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/1854/LU-8763889
http://doi.org/10.23919/EUSIPCO55093.2022.9909776
https://biblio.ugent.be/publication/8763889/file/8763891
https://biblio.ugent.be/publication/8763889

SureUnet: sparse autorepresentation encoder U-Net for noise artifact suppression in low-dose CT.

Autoren: Liu, Jin Zhang, Tingyu Kang, Yanqin et al.

Quelle: Neural Computing & Applications; Apr2025, Vol. 37 Issue 11, p7561-7573, 13p

Schlagwörter: COMPUTED tomography, IMAGE processing, IONIZING radiation, ARTIFICIAL intelligence, DEEP learning

Adaptive Forward-Backward Splitting for Multilayer Convolutional Dictionary Learning.

Autoren: Guan-Ju Peng

Quelle: SIAM Journal on Imaging Sciences; 2025, Vol. 18 Issue 1, p631-664, 34p

Schlagwörter: FORWARD-backward algorithm, DATA dictionaries, NONSMOOTH optimization, IMAGE reconstruction, IMAGE representation

Towards understanding residual and dilated dense neural networks via convolutional sparse coding.

Autoren: Zhang, Zhiyang Zhang, Shihua

Quelle: National Science Review; Mar2021, Vol. 8 Issue 3, p1-13, 13p

Schlagwörter: CONVOLUTIONAL neural networks, THRESHOLDING algorithms

Multilayer Convolutional Sparse Modeling: Pursuit and Dictionary Learning.

Autoren: Sulam, Jeremias Papyan, Vardan Romano, Yaniv et al.

Quelle: IEEE Transactions on Signal Processing; 8/1/2018, Vol. 66 Issue 15, p4090-4104, 15p

Schlagwörter: MULTILAYERS, ARTIFICIAL neural networks, FACTORIZATION, MATHEMATICAL bounds, ENCODING

Convolutional Dictionary Learning in Hierarchical Networks

Autoren: Zazo, Javier Tolooshams, Bahareh Ba, Demba

Index Begriffe: Computer Science - Machine Learning, Statistics - Machine Learning, text

URL: http://arxiv.org/abs/1907.09881

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

Multimodal Extension of the ML-CSC Framework for Medical Image Segmentation

Autoren: Shaoguang Huang Jens Janssens Aleksandra Pizurica et al.

Quelle: 2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA). :91-96

Zugangs-URL: https://biblio.ugent.be/publication/8716968
https://dblp.uni-trier.de/db/conf/imspa/ispa2021.html#JanssensLHP21

Multi-layer Convolutional Sparse Coding Framework for Restoration of Under-sampled MR Images

Autoren: Abdul Wahab Usman Ullah Abdul Wahid Kushairy Kadir et al.

Quelle: 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). :1-6

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://dblp.uni-trier.de/db/conf/i2mtc/i2mtc2021.html#WahidUKS21

MR‐DCAE: Manifold regularization‐based deep convolutional autoencoder for unauthorized broadcasting identification.

Autoren: Zheng, Qinghe Zhao, Penghui Zhang, Deliang et al.

Quelle: International Journal of Intelligent Systems; Dec2021, Vol. 36 Issue 12, p7204-7238, 35p

Schlagwörter: RADIO broadcasting, BROADCASTING industry, RADIO dramas

Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing.

Autoren: Monga, Vishal Li, Yuelong Eldar, Yonina C.

Quelle: IEEE Signal Processing Magazine; Mar2021, Vol. 38 Issue 2, p18-44, 27p

Convolutional Dictionary Learning in Hierarchical Networks

Autoren: Javier Zazo Bahareh Tolooshams Harvard John A. Paulson et al.

Quelle: 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). :131-135

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: http://arxiv.org/pdf/1907.09881
http://arxiv.org/pdf/1907.09881.pdf
https://dblp.uni-trier.de/db/journals/corr/corr1907.html#abs-1907-09881
https://arxiv.org/abs/1907.09881

A Convex Variational Model for Learning Convolutional Image Atoms from Incomplete Data.

Autoren: Chambolle, A. Holler, M. Pock, T.

Quelle: Journal of Mathematical Imaging & Vision; Apr2020, Vol. 62 Issue 3, p417-444, 28p

Adversarial Noise Attacks of Deep Learning Architectures: Stability Analysis via Sparse-Modeled Signals.

Autoren: Romano, Yaniv Aberdam, Aviad Sulam, Jeremias et al.

Quelle: Journal of Mathematical Imaging & Vision; Apr2020, Vol. 62 Issue 3, p313-327, 15p

Using Deep Neural Networks for Inverse Problems in Imaging: Beyond Analytical Methods.

Autoren: Lucas, Alice Iliadis, Michael Molina, Rafael et al.

Quelle: IEEE Signal Processing Magazine; Jan2018, Vol. 35 Issue 1, p20-36, 17p

A convolutional neural network with sparse representation.

Autoren: Yang, Guoan Yang, Junjie Lu, Zhengzhi et al.

Quelle: Knowledge-Based Systems. Dec2020, Vol. 209, pN.PAG-N.PAG. 1p.

Schlagwörter: *CONVOLUTIONAL neural networks, *SUPERVISED learning, *MACHINE learning, *FEATURE extraction, *WAVELET transforms, *NETWORK performance

Sparse Data–Driven Learning for Effective and Efficient Biomedical Image Segmentation.

Autoren: Onofrey, John A. Staib, Lawrence H. Huang, Xiaojie et al.

Quelle: Annual Review of Biomedical Engineering; Jun2020, Vol. 22, p127-153, 0p

Adaptive Radar Detection: Model-Based, Data-Driven and Hybrid Approaches

Autoren: Coluccia, Angelo

Resource Type: eBook.

Schlagworte: Radar--Automatic detection

Categories: TECHNOLOGY & ENGINEERING / Radar

Machine Learning : A Bayesian and Optimization Perspective

Autoren: Sergios Theodoridis

Resource Type: eBook.

Schlagworte: Mathematical optimization, Bayesian statistical decision theory, Machine learning--Mathematical models

Categories: TECHNOLOGY & ENGINEERING / Signals & Signal Processing, SCIENCE / Physics / Electromagnetism