Suchergebnisse - multilayer convolutional sparse coding (ML-CSC)

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

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

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

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

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

On Multi-Layer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks.

Autoren: Sulam, Jeremias Aberdam, Aviad Beck, Amir et al.

Quelle: IEEE Transactions on Pattern Analysis & Machine Intelligence; Aug2020, Vol. 42 Issue 8, p1968-1980, 13p

Schlagwörter: CONVOLUTIONAL neural networks, RECURRENT neural networks, THRESHOLDING algorithms, SUPERVISED learning, ALGORITHMS

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

CMCS-net: image compressed sensing with convolutional measurement via DCNN.

Autoren: Xie, Yahong Wang, Hailin Wang, Jianjun

Quelle: IET Image Processing (Wiley-Blackwell); 2020, Vol. 14 Issue 15, p3839-3850, 12p

Schlagwörter: DEEP learning, CONVOLUTIONAL neural networks, PROBLEM solving, COMPRESSED sensing, SIGNAL convolution, COMPUTATIONAL complexity, DATA compression

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

Learning the sparse prior: Modern approaches.

Autoren: Peng, Guan‐Ju

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

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

Effect of Top-Down Connections in Hierarchical Sparse Coding.

Autoren: Boutin, Victor Franciosini, Angelo Ruffier, Franck et al.

Quelle: Neural Computation; Nov2020, Vol. 32 Issue 11, p2279-2309, 31p, 5 Color Photographs, 3 Black and White Photographs, 2 Diagrams, 2 Charts, 8 Graphs

Schlagwörter: FORECASTING, DATA structures, CIPHERS, MULTIDIMENSIONAL databases

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

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

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

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

Solving inverse problems using data-driven models.

Autoren: Arridge, Simon Maass, Peter Öktem, Ozan et al.

Quelle: Acta Numerica; May2019, Vol. 28 Issue 8, p1-174, 174p

New Generation Artificial Intelligence-Driven Diagnosis and Maintenance Techniques

Autoren: Guangrui Wen Zihao Lei Xuefeng Chen et al.

Resource Type: eBook.

Schlagworte: Artificial intelligence--Industrial applications, Machinery--Maintenance and repair--Data processing

Categories: COMPUTERS / Artificial Intelligence / General, SCIENCE / Physics / General, TECHNOLOGY & ENGINEERING / Engineering (General), TECHNOLOGY & ENGINEERING / Manufacturing

Computer Vision – ACCV 2018

Autoren: C. V. Jawahar Hongdong Li Greg Mori et al.

Resource Type: eBook.

Schlagworte: Computer vision, Artificial intelligence, Computer engineering, Computer networks, Computer science—Mathematics

Categories: COMPUTERS / Artificial Intelligence / Computer Vision & Pattern Recognition, COMPUTERS / Artificial Intelligence / General, COMPUTERS / Computer Science, COMPUTERS / Hardware / General, MATHEMATICS / Applied