Výsledky vyhledávání - sparse convolutional autoencoder (space)*

Leveraging variant of CAE with sparse convolutional embedding and two-stage application-driven data augmentation for image clustering.

Autoři: Liu, Yanming Liu, Jinglei

Zdroj: Soft Computing - A Fusion of Foundations, Methodologies & Applications. Feb2025, Vol. 29 Issue 3, p1419-1435. 17p.

Témata: *DATA augmentation, *AUTOENCODERS

Deep-Learning-Based Dictionary Construction for MIMO Radar Detection in Complex Scenes

Autoři: Hongyan Wang He Zhou Qinghua Guo a další

Zdroj: IEEE Internet of Things Journal. 11:12419-12434

Témata: nonlinear correction, Multiple-input multiple-output (MIMO) radar, transceiving space-time resource allocation, sparse representation, complex-valued convolutional autoencoder

A novel multichannel sparse convolutional autoencoder for electrocardiogram signal compression.

Autoři: Bekiryazıcı, Tahir Damkacı, Mehmet Aydemir, Gürkan a další

Zdroj: Journal of Electrocardiology. Nov2025, Vol. 93, pN.PAG-N.PAG. 1p.

Reconstructing flow field from sparse sensor data: A deep learning framework combining autoencoder and cross-attention mechanism.

Autoři: Kong, Xuan Fan, Qipei Li, Jinzhao a další

Zdroj: Physics of Fluids. Sep2025, Vol. 37 Issue 9, p1-12. 12p.

Témata: *AUTOENCODERS, *MACHINE learning, *AERODYNAMIC measurements, *ATMOSPHERIC models, *DETECTORS

DOA estimation method for sparse arrays based on deep convolutional autoencoder and deep convolutional neural network.

Autoři: Guo, Shuhan Zhang, Qin Fu, Xiaolong a další

Zdroj: Digital Signal Processing. Jan2026:Part D, Vol. 168, pN.PAG-N.PAG. 1p.

Témata: *DIRECTION of arrival estimation, *CONVOLUTIONAL neural networks, *SPARSE matrices, *MULTIPLE Signal Classification, *FEATURE extraction, *COVARIANCE matrices, *ESTIMATION theory

3D Segmentation of Perivascular Spaces on T1-Weighted 3 Tesla MR Images With a Convolutional Autoencoder and a U-Shaped Neural Network

Autoři: Philippe Boutinaud Ami Tsuchida Alexandre Laurent a další

Zdroj: Frontiers in Neuroinformatics, Vol 15 (2021)

Témata: perivascular space, deep learning, U-net, MRI, brain cohort, segmentation, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571

Popis souboru: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/fninf.2021.641600/full; https://doaj.org/toc/1662-5196

Přístupová URL adresa: https://doaj.org/article/ed3360c8420941b78108600cebb4efc3

Application of a Convolutional Autoencoder to Half Space Radar Hrrp Recognition

Autoři: Yong-Jun Xie Shi-Hao Yu

Zdroj: 2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). :48-53

Témata: 2. Zero hunger, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 3. Good health

Přístupová URL adresa: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8521306
https://ieeexplore.ieee.org/document/8521306/

Unsupervised learning with sparse space-and-time autoencoders

Autoři: Graham, Benjamin

Témata: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, text

URL: http://arxiv.org/abs/1811.10355

3D segmentation of perivascular spaces on T1-weighted 3 Tesla MR images with a convolutional autoencoder and a U-shaped neural network

Autoři: Boutinaud, P. Tsuchida, A. Laurent, A. a další

Zdroj: Frontiers in Neuroinformatics

Popis souboru: application/pdf

Přístupová URL adresa: http://hdl.handle.net/21.11116/0000-000B-12F0-1
http://hdl.handle.net/21.11116/0000-000B-12EE-5

3D segmentation of perivascular spaces on T1-weighted 3 Tesla MR images with a convolutional autoencoder and a U-shaped neural network

Autoři: Philippe Boutinaud Ami Tsuchida Alexandre Laurent a další

Zdroj: Front Neuroinform
Frontiers in Neuroinformatics, Vol 15 (2021)

Témata: 03 medical and health sciences, 0302 clinical medicine, segmentation, deep learning, Neurosciences. Biological psychiatry. Neuropsychiatry, perivascular space, U-net, brain cohort, MRI, RC321-571, Neuroscience

Přístupová URL adresa: https://oskar-bordeaux.fr/bitstream/20.500.12278/110227/1/BPH_FN_2021_Boutinaud.pdf
https://www.frontiersin.org/articles/10.3389/fninf.2021.641600/pdf
https://pubmed.ncbi.nlm.nih.gov/34262443
https://doaj.org/article/ed3360c8420941b78108600cebb4efc3
https://biorxiv.org/content/10.1101/2020.11.25.397364v1.full.pdf
https://www.biorxiv.org/content/10.1101/2020.11.25.397364v1

Real-time forecasting of chaotic dynamics from sparse data and autoencoders

Autoři: Özalp, Elise Nóvoa, Andrea Magri, Luca

Témata: 37N10, 65P20, Chaotic Dynamics, FOS: Physical sciences, Chaotic Dynamics (nlin.CD)

Přístupová URL adresa: http://arxiv.org/abs/2508.08729

Convolutional autoencoder-based color image classification using chroma subsampling in YCbCr space

Autoři: Zuhe Li Fengqin Wang Yangyu Fan

Zdroj: 2015 8th International Congress on Image and Signal Processing (CISP). :351-355

Témata: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 10. No inequality

Přístupová URL adresa: https://ieeexplore.ieee.org/document/7407903/

A novel intelligent model for high-precision reconstruction of dam-break free surface profiles from sparse monitoring data.

Autoři: Deng, Yangyu Liu, Yakun Zhang, Di a další

Zdroj: Physics of Fluids. Feb2025, Vol. 37 Issue 2, p1-28. 28p.

Témata: *PROPER orthogonal decomposition, *WATER levels, *AUTOENCODERS, *FREE surfaces, *FLOOD control

Temperature scaling unmixing framework based on convolutional autoencoder

Autoři: Jin Xu Mingming Xu Shanwei Liu a další

Zdroj: International Journal of Applied Earth Observations and Geoinformation, Vol 129, Iss , Pp 103864- (2024)

Témata: Hyperspectral unmixing, Convolutional autoencoder, Sparsity constraint, Temperature scaling, Equal-frequency binning, Physical geography, GB3-5030, Environmental sciences, GE1-350

Popis souboru: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S1569843224002188; https://doaj.org/toc/1569-8432

Přístupová URL adresa: https://doaj.org/article/aa7533b7fbf447e1a8b51942e4c8c5f4

Sparse Representations for Object- and Ego-Motion Estimations in Dynamic Scenes

Autoři: Hirak J. Kashyap Charless C. Fowlkes Jeffrey L. Krichmar

Zdroj: IEEE Transactions on Neural Networks and Learning Systems, vol 32, iss 6

Témata: FOS: Computer and information sciences, Artificial intelligence, Computer Science - Machine Learning, Computer Vision and Pattern Recognition (cs.CV), cs.LG, Computer Science - Computer Vision and Pattern Recognition, ego motion, 02 engineering and technology, Computer Vision and Multimedia Computation, Optical imaging, Machine Learning (cs.LG), Information and Computing Sciences, 0202 electrical engineering, electronic engineering, information engineering, Training, Artificial Intelligence & Image Processing, object motion, sparse representation, cs.CV, overcomplete basis, Cameras, 16. Peace & justice, Dynamics, Convolutional autoencoder, Motion segmentation, Estimation, Adaptive optics

Popis souboru: application/pdf

Přístupová URL adresa: https://escholarship.org/content/qt7p53p8zv/qt7p53p8zv.pdf?t=qgb7mw
https://pubmed.ncbi.nlm.nih.gov/32687472
http://arxiv.org/abs/1903.03731
https://europepmc.org/article/MED/32687472
https://arxiv.org/pdf/1903.03731.pdf
https://arxiv.org/abs/1903.03731
https://par.nsf.gov/biblio/10191836-sparse-representations-object-ego-motion-estimations-dynamic-scenes
https://pubmed.ncbi.nlm.nih.gov/32687472/
https://ui.adsabs.harvard.edu/abs/2019arXiv190303731K/abstract
https://escholarship.org/uc/item/7p53p8zv

Autoencoders for Semi-Supervised Water Level Modeling in Sewer Pipes with Sparse Labeled Data

Autoři: Rius, Ferran Plana Philipsen, Mark P. Tur, Josep Maria Mirats a další

Zdroj: Rius, F P, Philipsen, M P, Tur, J M M, Moeslund, T B, Bahón, C A & Casas, M 2022, 'Autoencoders for Semi-Supervised Water Level Modeling in Sewer Pipes with Sparse Labeled Data', Water (Switzerland), vol. 14, no. 3, 333. https://doi.org/10.3390/w14030333

Témata: Autoencoders and latent space, Data distribution, Self-supervised, Semi-supervised, Sparse data, Supervised, Water

Popis souboru: application/pdf

Relation: info:eu-repo/semantics/altIdentifier/pissn/2073-4441

Dostupnost: https://vbn.aau.dk/da/publications/65dbf42a-774e-45d2-906a-47e7bd52a17d
https://doi.org/10.3390/w14030333
https://vbn.aau.dk/ws/files/482740247/water_14_00333.pdf
https://www.scopus.com/pages/publications/85123710424

Deep Learning Augmented Data Assimilation: Reconstructing Missing Information With Convolutional Autoencoders

Autoři: Wang, Yueya Shi, Xiaoming Lei, Lili a další

Témata: Article

URL: http://repository.ust.hk/ir/Record/1783.1-116983
http://lbdiscover.ust.hk/uresolver?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rfr_id=info:sid/HKUST:SPI&rft.genre=article&rft.issn=&rft.volume=&rft.issue=&rft.date=2022&rft.spage=&rft.aulast=Wang&rft.aufirst=Yueya&rft.atitle=Deep+Learning+Augmented+Data+Assimilation%3A+Reconstructing+Missing+Information+With+Convolutional+Autoencoders&rft.title=Monthly+weather+review

Applying Convolutional Neural Networks to data on unstructured meshes with space-filling curves.

Autoři: Heaney, Claire E. Li, Yuling Matar, Omar K. a další

Zdroj: Neural Networks. Jul2024, Vol. 175, pN.PAG-N.PAG. 1p.

Témata: *CONVOLUTIONAL neural networks, *IMAGE recognition (Computer vision), *TRAVELING salesman problem, *RECURRENT neural networks, *IMAGE compression, *PARTIAL differential equations, *FINITE element method

3D Segmentation of Perivascular Spaces on T1-Weighted 3 Tesla MR Images With a Convolutional Autoencoder and a U-Shaped Neural Network.

Autoři: Boutinaud, Philippe Tsuchida, Ami Laurent, Alexandre a další

Zdroj: Frontiers in Neuroinformatics; 6/18/2021, Vol. 15, p1-21, 21p

Témata: MAGNETIC resonance imaging, DEEP learning, ALGORITHMS, BASAL ganglia, YOUNG adults, VOXEL-based morphometry

Applying Convolutional Neural Networks to Data on Unstructured Meshes with Space-Filling Curves

Autoři: Heaney, Claire E. Li, Yuling Matar, Omar K. a další

Témata: Mathematics - Numerical Analysis, Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2011.14820