Suchergebnisse - CNN Overcomplete Autoencoder inference errors analysis

Moving forward in water distribution network leak identification through an innovative features engineering step.

Autoren: Damonti, Elvio Bernasconi, Giancarlo

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

Schlagwörter: *WATER distribution, *LEAK detection, *FEATURE selection, *PATTERN perception, *AUTOENCODERS, *CONVOLUTIONAL neural networks, *OUTLIER detection

Deep Medical Image Reconstruction with Autoencoders using Deep Boltzmann Machine Training.

Autoren: S., Saravanan Juliet, Sujitha

Quelle: EAI Endorsed Transactions on Pervasive Health & Technology; 2020, Vol. 6 Issue 24, p1-9, 9p

Schlagwörter: MACHINE learning, LOGISTIC regression analysis, STAKEHOLDERS, COMPARATIVE studies, ELECTRONIC health records

Overcomplete graph convolutional denoising autoencoder for noisy skeleton action recognition.

Autoren: Guo, Jiajun Ji, Qingge Shan, Guangwei

Quelle: IET Image Processing (Wiley-Blackwell); 1/10/2024, Vol. 18 Issue 1, p233-246, 14p

Schlagwörter: SIGNAL denoising, COMPUTER vision

A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes Classification.

Autoren: Kadhim, Yezi Ali Guzel, Mehmet Serdar Mishra, Alok

Quelle: Diagnostics (2075-4418); Jul2024, Vol. 14 Issue 14, p1469, 25p

Schlagwörter: COMPUTER-aided diagnosis, METAHEURISTIC algorithms, CONVOLUTIONAL neural networks, DEEP learning, COMPUTER science

Model-based Bayesian Fusion-Net for infrared and visible image fusion.

Autoren: Li, Wang Yafang, Kuang Ziyi, Cai et al.

Quelle: EURASIP Journal on Image & Video Processing; 8/6/2025, Vol. 2025 Issue 1, p1-25, 25p

Schlagwörter: IMAGE fusion, INFRARED photography, COMPUTATIONAL complexity, QUANTITATIVE research, IMAGE processing

eXplainable AI for Word Embeddings: A Survey.

Autoren: Boselli, Roberto D'Amico, Simone Nobani, Navid

Quelle: Cognitive Computation; Feb2025, Vol. 17 Issue 1, p1-24, 24p

Efficient Diffusion Posterior Sampling for Noisy Inverse Problems.

Autoren: Li, Ji Wang, Chao

Quelle: SIAM Journal on Imaging Sciences; 2025, Vol. 18 Issue 2, p1468-1492, 25p

Learning Latent Representations of 3D Human Pose with Deep Neural Networks.

Autoren: Katircioglu, Isinsu Tekin, Bugra Salzmann, Mathieu et al.

Quelle: International Journal of Computer Vision; Dec2018, Vol. 126 Issue 12, p1326-1341, 16p, 2 Color Photographs, 7 Diagrams, 10 Charts, 1 Graph

Schlagwörter: DEEP learning, ARTIFICIAL neural networks, POSE estimation (Computer vision), COMPUTER vision, IMAGE reconstruction

基于逆自编码器和通道注意力机制的大地电磁信号去噪方法. (Chinese)

Alternate Title: Electromagnetic signal denoising method based on inverse autoencoder and channel attention mechanism. (English)

Autoren: 余年 计铭杰 张超

Quelle: Reviews of Geophysics & Planetary Physics; 2025, Vol. 56 Issue 6, p665-673, 9p

Schlagwörter: SIGNAL denoising, AUTOENCODERS, ELECTROMAGNETIC waves, MAGNETOTELLURIC prospecting, ARTIFICIAL neural networks, GEOPHYSICAL prospecting, DATA quality

Top-down perceptual inference shaping the activity of early visual cortex.

Autoren: Csikor, Ferenc Meszéna, Balázs Ócsai, Katalin et al.

Quelle: Nature Communications; 11/14/2025, Vol. 16 Issue 1, p1-23, 23p

Schlagwörter: VISUAL cortex, TEXTURE analysis (Image processing), SENSORY perception, GENERATIVE artificial intelligence, VISUAL perception, SYNAPSES, COGNITIVE psychology

Emerging deep learning approaches for urban satellite image analysis: a survey on classification, segmentation, and change detection.

Autoren: Soni, Tannu Kumar Pujari, Pushpalata

Quelle: Evolutionary Intelligence; Oct2025, Vol. 18 Issue 5, p1-45, 45p

Signal Preprocessing, Decomposition and Feature Extraction Methods in EEG-Based BCIs.

Autoren: Mdluli, Bandile Khumalo, Philani Maswanganyi, Rito Clifford

Quelle: Applied Sciences (2076-3417); Nov2025, Vol. 15 Issue 22, p12075, 36p

Schlagwörter: SIGNAL processing, FEATURE extraction, MACHINE learning, WAVELETS (Mathematics), ELECTROENCEPHALOGRAPHY, BRAIN-computer interfaces, INDEPENDENT component analysis, DEEP learning

Autoencoder-Based Poisoning Attack Detection in Graph Recommender Systems.

Autoren: Zhou, Quanqiang Zhao, Xi Zhang, Xiaoyue

Quelle: Information; Nov2025, Vol. 16 Issue 11, p1004, 34p

Schlagwörter: AUTOENCODERS, RECOMMENDER systems, LOSS functions (Statistics), STATISTICS, MACHINE learning

A Comprehensive Review of Multimodal Visual Representation Learning: Tracing the Evolution from CNNs to Transformers and Beyond.

Autoren: Zhang, Dong Wong, W. K. Chew, I. M.

Quelle: International Journal of Multimedia Information Retrieval; Dec2025, Vol. 14 Issue 4, p1-30, 30p

Fast Anomaly Detection for Vision-Based Industrial Inspection Using Cascades of Null Subspace PCA Detectors.

Autoren: Bilal, Muhammad Hanif, Muhammad Shehzad

Quelle: Sensors (14248220); Aug2025, Vol. 25 Issue 15, p4853, 27p

Schlagwörter: OUTLIER detection, PRINCIPAL components analysis, COMPUTER vision, CONVOLUTIONAL neural networks, MANUFACTURING process automation, MATHEMATICAL optimization, QUALITY assurance

Residual encoder-decoder based architecture for medical image denoising.

Autoren: Ferdi, Abdesselam Benierbah, Said Nakib, Amir

Quelle: Multimedia Tools & Applications; Jun2025, Vol. 84 Issue 19, p21625-21642, 18p

Schlagwörter: IMAGE processing, COMPUTED tomography, ARTIFICIAL intelligence, DIAGNOSTIC imaging, IMAGE denoising, X-rays, DEEP learning

Enhancing Border Learning for Better Image Denoising.

Autoren: Ge, Xin Zhu, Yu Qi, Liping et al.

Quelle: Mathematics (2227-7390); Apr2025, Vol. 13 Issue 7, p1119, 30p

Schlagwörter: ARTIFICIAL neural networks, CONVOLUTIONAL neural networks, AUTOENCODERS, IMAGE denoising, LEARNING ability

AnoSense: Edge computing for real-time flight anomaly detection by using embedded deep neural networks.

Alternate Title: AnoSense: Gömülü derin sinir ağları kullanarak gerçek zamanlı uçuş anormalliklerinin tespiti için uç birim hesaplama. (Turkish)

Autoren: DUDUKCU, Hatice Vildan TASKIRAN, Murat KAHRAMAN, Nihan

Quelle: Gümüshane Üniversitesi Fen Bilimleri Enstitüsü Dergisi; 2025, Vol. 15 Issue 3, p797-808, 12p

Schlagwörter: EDGE computing, AERONAUTICAL safety measures, EMBEDDED computer systems, ARTIFICIAL neural networks, AUTONOMOUS robots, DETECTORS, RELIABILITY in engineering, REAL-time computing

A novel state of health estimation model for lithium-ion batteries incorporating signal processing and optimized machine learning methods.

Autoren: Zhang, Xing Feng, Juqiang Cai, Feng et al.

Quelle: Frontiers in Energy; Jun2025, Vol. 19 Issue 3, p348-364, 17p

Multiscale CNN With Autoencoder Regularization Joint Contextual Attention Network for SAR Image Classification.

Autoren: Wu, Zitong Hou, Biao Jiao, Licheng

Quelle: IEEE Transactions on Geoscience & Remote Sensing; Feb2021, Vol. 59 Issue 2, p1200-1213, 14p

Schlagwörter: SYNTHETIC aperture radar, SIGNAL convolution, CONVOLUTIONAL neural networks, SUCCESSIVE approximation analog-to-digital converters, IMAGE analysis, DEEP learning, CLASSIFICATION algorithms