Suchergebnisse - sparse convolutional autoencoder ((sage OR space))

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

Autoren: Liu, Yanming Liu, Jinglei

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

Schlagwörter: *DATA augmentation, *AUTOENCODERS

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

Autoren: Hongyan Wang He Zhou Qinghua Guo et al.

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

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

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

Autoren: Kong, Xuan Fan, Qipei Li, Jinzhao et al.

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

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

A novel multichannel sparse convolutional autoencoder for electrocardiogram signal compression.

Autoren: Bekiryazıcı, Tahir Damkacı, Mehmet Aydemir, Gürkan et al.

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

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

Autoren: Guo, Shuhan Zhang, Qin Fu, Xiaolong et al.

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

Schlagwörter: *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

Autoren: Philippe Boutinaud Ami Tsuchida Alexandre Laurent et al.

Quelle: Frontiers in Neuroinformatics, Vol 15 (2021)

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

Dateibeschreibung: electronic resource

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

Zugangs-URL: https://doaj.org/article/ed3360c8420941b78108600cebb4efc3

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

Autoren: Deng, Yangyu Liu, Yakun Zhang, Di et al.

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

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

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

Autoren: Özalp, Elise Nóvoa, Andrea Magri, Luca

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

Zugangs-URL: http://arxiv.org/abs/2508.08729

Application of a Convolutional Autoencoder to Half Space Radar Hrrp Recognition

Autoren: Yong-Jun Xie Shi-Hao Yu

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

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

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

Temperature scaling unmixing framework based on convolutional autoencoder

Autoren: Jin Xu Mingming Xu Shanwei Liu et al.

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

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

Dateibeschreibung: electronic resource

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

Zugangs-URL: https://doaj.org/article/aa7533b7fbf447e1a8b51942e4c8c5f4

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

Autoren: Boutinaud, P. Tsuchida, A. Laurent, A. et al.

Quelle: Frontiers in Neuroinformatics

Dateibeschreibung: application/pdf

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

Application of Artificial Intelligence Technologies as an Intervention for Promoting Healthy Eating and Nutrition in Older Adults: A Systematic Literature Review.

Autoren: Kalu, Kingsley Ataguba, Grace Onifade, Oyepeju et al.

Quelle: Nutrients. Oct2025, Vol. 17 Issue 20, p3223. 33p.

Unsupervised learning with sparse space-and-time autoencoders

Autoren: Graham, Benjamin

Index Begriffe: 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

Autoren: Philippe Boutinaud Ami Tsuchida Alexandre Laurent et al.

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

Schlagwörter: 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

Zugangs-URL: 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

Automated Video-EEG Analysis in Epilepsy Studies: A Narrative Review of Advances and Challenges.

Autoren: Zuev VA Salmagambetova EG Djakov SN et al.

Quelle: Journal of medical systems [J Med Syst] 2025 Oct 20; Vol. 49 (1), pp. 142. Date of Electronic Publication: 2025 Oct 20.

Publikationsart: Journal Article; Review

Info zur Zeitschrift: Publisher: Kluwer Academic/Plenum Publishers Country of Publication: United States NLM ID: 7806056 Publication Model: Electronic Cited Medium: Internet ISSN: 1573-689X (Electronic) Linking ISSN: 01485598 NLM ISO Abbreviation: J Med Syst Subsets: MEDLINE

MeSH-Schlagworte: Electroencephalography*/methods , Epilepsy*/diagnosis , Epilepsy*/physiopathology , Video Recording*/methods, Humans ; Machine Learning

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

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

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

Schlagwörter: 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

Dateibeschreibung: application/pdf

Zugangs-URL: 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

Autoren: Rius, Ferran Plana Philipsen, Mark P. Tur, Josep Maria Mirats et al.

Quelle: 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

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

Dateibeschreibung: application/pdf

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

Verfügbarkeit: 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

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

Autoren: Zuhe Li Fengqin Wang Yangyu Fan

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

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

Zugangs-URL: https://ieeexplore.ieee.org/document/7407903/

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

Autoren: Wang, Yueya Shi, Xiaoming Lei, Lili et al.

Quelle: Monthly Weather Review. 150:1977-1991

Schlagwörter: 13. Climate action, Data assimilation, 01 natural sciences, 0105 earth and related environmental sciences

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

Autoren: Heaney, Claire E. Li, Yuling Matar, Omar K. et al.

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

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