• Suche: "Semi-Supervised Learning"

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Expert System Based on Ontology and Interpretable Machine Learning to Assist in the Discovery of Railway Accident Scenarios
Système expert basé sur l'ontologie et l'apprentissage automatique interprétable pour aider à la découverte de scénarios d'accidents ferroviaires
Hadj-Mabrouk, Habib ; Université Gustave Eiffel ; Vice-Présidence Recherche (VP Recherche)
CMC-Computers. :1-32

Artificial intelligence ontology semi-supervised learning expert system association rules railways …
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2

A Unified Contrastive Loss for Self-Training
Gauffre, Aurélien ; Horvat, Julien ; Amini, Massih-Reza ; et al.
Machine Learning and Knowledge Discovery in Databases. Research Track and Demo Track - European Conference. :3-18

Vilnius, Lithuania Self-Training Classification Contrastive Learning Semi-Supervised Learning Semi-Supervised Learning... …
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4

On Diversity in Discriminative Neural Networks
Oubaha, Brahim ; Berrou, Claude ; Ji, Xueyao ; et al.
ISIVC 2024: IEEE 12th International Symposium on Signal. :1-6

Marrakech, Morocco Neural network diversity... Neural network diversity competition sparsity …
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5

Robust deep MRI contrast synthesis using a prior-based and task-oriented 3D network
Morell-Ortega, Sergio ; Ruiz-Perez, Marina ; Gadea, Marien ; et al.
Imaging Neuroscience, 2025, 3, ⟨10.1162/IMAG.a.116⟩

semi-supervised learning deep learning contrast synthesis MRI MRI contrast synthesis d... [INFO.INFO-IM]Computer S... …
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6

SECL: A Zero-Day Attack Detector and Classifier based on Contrastive Learning and Strong Regularization
Duraz, Robin ; Espes, David ; Francq, Julien ; et al.
ARES 2024: The 19th International Conference on Availability. :1-12

Vienna, Austria Security and privacy → I... • Computing methodologie... Zero-Day Detection Zero-Day Classification Contrastive Learning …
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7

Federated Intrusion Detection in Medical IoT: Client-Side Feature Learning with Variational Autoencoders vs Autoencoders
Harhad, Ahlem ; Drocourt, Cyril ; Durand, David ; et al.

Medical IoT β-VAE Semi-Supervised Learning Federated Learning Intrusion Detection Syst... [INFO.INFO-MO]Computer S... …
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8

QUEST: Quality-Aware Semi-supervised Table Extraction for Business Documents
Thomas, Eliott ; Coustaty, Mickaël ; Joseph, Aurélie ; et al.
ICDAR 2025. :279-302

Wuhan / Chine, China Quality Assessment Semi-Supervised Learning Table Extraction Table Extraction Semi-Su... [INFO.INFO-AI]Computer S... …
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9

Almost exact recovery in noisy semi-supervised learning
Avrachenkov, Konstantin ; Dreveton, Maximilien ; Network Engineering and Operations (NEO) ; et al.
Probability in the Engineering and Informational Sciences. :1-22

Degree corrected stochas... Graph-based method Graph clustering Noisy data Semi-supervised learning... Secondary 62H30 …
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10

Shape Transformation Driven by Active Contour for Class-Imbalanced Semi-Supervised Medical Image Segmentation
Gu, Yuliang ; Liu, Yepeng ; Sun, Zhichao ; et al.
2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Dec 2024, Lisbon (Portugal), Portugal

Lisbon (Portugal), Portu... Semi-supervised learning Semi-supervised learning... Data augmentation Class imbalance 3D medical image segment... …
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11

'LOW SUPERVISION' DEEP CLUSTER CHANGE DETECTION (CDCLUSTER) ON REMOTE SENSING RGB DATA: TOWARDS THE UNSUPERVISING CLUSTERING FRAMEWORK
Garcia, Guglielmo Fernandez ; de Gelis, Iris ; Corpetti, Thomas ; et al.
IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium. :6656-6659

Pasadena, United States Multitemporal images Deep learning Clustering Unsupervised Learning Semi-supervised Learning …
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12

Learning UAV-Based Above-Ground Biomass Regression Models in Sparse Training Data Environments
Kröber, F. ; Garcia, G. Fernandez ; Guiotte, F. ; et al.
IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium. :3322-3325

Pasadena, United States UAV transfer learning semi-supervised learning deep learning vegetation biomass regre... …
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13

Assessing the Effectiveness of Supervised and Semi-supervised NILM Approaches in an Industrial Context
Akbar, Mohammad Kaosain ; Amayri, Manar ; Bouguila, Nizar ; et al.
CIIS 2023: 6th International Conference on Computational Intelligence and Intelligent Systems. :7-13

Tokyo Japan, France NILM Energy Disaggregation Supervised learning Semi-supervised learning Deep Learning …
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14

Multi-class Probabilistic Bounds for Majority Vote Classifiers with Partially Labeled Data
Feofanov, Vasilii ; Devijver, Emilie ; Amini, Massih-Reza ; et al.
Journal of Machine Learning Research. 25(104):1-47

self-training transductive inference multi-class classificati... learning theory semi-supervised learning semi-supervised learning... …
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15

Generalized pseudo-labeling in consistency regularization for semi-supervised learning
Karaliolios, Nikolaos ; Chabot, Florian ; Dupont, Camille ; et al.
ICIP 2023 - 2023 IEEE International Conference on Image Processing. :525-529

Kuala Lumpur, Malaysia Costs Error analysis Annotations Semantic segmentation Image processing …
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16

A soft nearest-neighbor framework for continual semi-supervised learning
Kang, Zhiqi ; Fini, Enrico ; Nabi, Moin ; et al.
ICCV 2023 - IEEE/CVF International Conference on Computer Vision. :11834-11843

Paris, France Semi-supervised learning Image classification Continual learning ACM: I.: Computing Metho... I.2: ARTIFICIAL INTELLIG... …
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17

Toulouse Hyperspectral Data Set: A benchmark data set to assess semi-supervised spectral representation learning and pixel-wise classification techniques
Ensemble de données hyperspectrales de Toulouse : un ensemble de données de référence pour évaluer l'apprentissage de la représentation spectrale semi-supervisée et les techniques de classification par pixel
Thoreau, Romain ; Risser, Laurent ; Achard, Véronique ; et al.
ISPRS Journal of Photogrammetry and Remote Sensing. 212:323-337

Self-supervised learning Semi-supervised learning Benchmark data set Land cover mapping Hyperspectral imaging [PHYS]Physics [physics] …
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18

Semi-Supervised Multimodal Representation Learning Through a Global Workspace
Devillers, Benjamin ; Maytié, Léopold ; Vanrullen, Rufin ; et al.
IEEE Transactions on Neural Networks and Learning Systems. 36(5):7843-7857

semi-supervised learning multimodal learning global workspace (GW) th... Cycle-consistency MESH: Algorithms MESH: Animals …
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19

Boosting grapevine phenological stages prediction based on climatic data by pseudo-labeling approach
Mehdi Fasihi ; Mirko Sodini ; Alex Falcon ; et al.
Artificial Intelligence in Agriculture, Vol 15, Iss 3, Pp 550-563 (2025)

Pseudo-labeling approach Climatic data Semi-supervised learning Machine learning Agriculture Grapevine phenological s... …
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20

Enhanced deep-style interpreter for automatic synthesis of annotated medical images
Marcos Sergio Pacheco dos Santos Lima Junior ; Juan Miguel Ortiz-de-Lazcano-Lobato ; José David Fernández-Rodríguez ; et al.
RIUMA. Repositorio Institucional de la Universidad de Málaga
Universidad de Málaga

semi-supervised learning Aprendizaje automatico Aprendizaje automático (... Redes neuronales artific... Reconocimiento de formas... Sistemas de imágenes en... …
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