Suchergebnisse - Depthwise convolution*

Classification of Petrographic Thin Section Images With Depthwise Separable Convolution and Dilated Convolution

Autoren: Shaowei Pan Xingxing Cheng Wenjing Fan

Quelle: IEEE Access, Vol 13, Pp 28789-28798 (2025)

Schlagwörter: depthwise separable convolution, classification, Petrographic thin section image, attention module, Electrical engineering. Electronics. Nuclear engineering, dilated convolution, TK1-9971

Zugangs-URL: https://doaj.org/article/69a34c618e614cd78ffb1c852bbba57e

High-Throughput Adaptive Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning

Autoren: Mostafa Naseri Eli De Poorter Ingrid Moerman et al.

Quelle: IEEE Open Journal of the Communications Society, Vol 6, Pp 656-670 (2025)
IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY
IEEE Open Journal of the Communications Society

Schlagwörter: Technology and Engineering, depthwise separable convolution, Co-channel interference cancellation, Telecommunication, convolutional neural network, quantization, TK5101-6720, resource-constrained environments, Transportation and communications, HE1-9990

Dateibeschreibung: application/pdf

Zugangs-URL: https://doaj.org/article/341cafeff36d4bfda9e1787bac74b2c1
http://doi.org/10.1109/OJCOMS.2024.3523797
https://biblio.ugent.be/publication/01JK5HTAWDGWMJJS2WJXFZYXPX/file/01JK5HYN9MJFV42CWNKWW2Q23F
http://hdl.handle.net/1854/LU-01JK5HTAWDGWMJJS2WJXFZYXPX
https://biblio.ugent.be/publication/01JK5HTAWDGWMJJS2WJXFZYXPX

Explainable AI for Lightweight Network Traffic Classification Using Depthwise Separable Convolutions

Autoren: Mustafa Ghaleb Mosab Hamdan Abdulaziz Y. Barnawi et al.

Quelle: IEEE Open Journal of the Computer Society, Vol 6, Pp 908-920 (2025)

Schlagwörter: depthwise separable convolution, SHAP, Electronic computers. Computer science, eXplainable artificial intelligence, Deep learning, network traffic classification, QA75.5-76.95, Information technology, LIME, T58.5-58.64

Zugangs-URL: https://doaj.org/article/d8d3502350f944ea89517d8870acbef6

Adaptive personalized federated learning with lightweight depthwise convolutional bottleneck network for novel intrusion detection system in internet of vehicles

Autoren: Shahad Almansour Kusum Yadav Lulwah M. Alkwai et al.

Quelle: Scientific Reports, Vol 15, Iss 1, Pp 1-14 (2025)

Schlagwörter: Internet of vehicles (IoV), Adaptive personalized federated learning (APFed), Intrusion detection system (IDS), Lightweight depthwise convolutional bottleneck network (LDwCBN), Non-IID data, Medicine, Science

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2045-2322

Zugangs-URL: https://doaj.org/article/9d54a216e5f048f7a03c451914dab1e4

Lightweight Unet with depthwise separable convolution for skin lesion segmentation

Autoren: Yong Li Bosheng Hu Xia Wang et al.

Quelle: Scientific Reports, Vol 15, Iss 1, Pp 1-17 (2025)

Schlagwörter: Skin segmentation, Lightweight model, Depthwise separable convolution, Medicine, Science

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2045-2322

Zugangs-URL: https://doaj.org/article/55386581192542628fd2e649479d930d

DSC-SNN: A Depthwise Separable Convolutional Spiking Neural Network for Efficient, Privacy-Preserving Action Recognition from Event-Based Data

Autoren: Nguyen Tan Khang Le Ha Dung Nguyen Thanh Binh Nguyen

Quelle: 2025 International Conference on Multimedia Analysis and Pattern Recognition (MAPR). :1-6

A Lightweight Modulation Recognition Network with Depthwise Separable Convolution and SNR Enhancement

Autoren: Jingyi Xing Shengfa Xu Chunpeng Xu et al.

Quelle: 2025 7th International Conference on Electronics and Communication, Network and Computer Technology (ECNCT). :530-534

Secure Routing and Attack Detection in Manets; a Hybrid Optimization Approach Using Depthwise Separable Convolutional Neural Networks

Autoren: Raghu Dhumpati Ruchira Rawat Rajesh L et al.

Quelle: 2025 6th International Conference on Data Intelligence and Cognitive Informatics (ICDICI). :1131-1137

DS-AdaptNet: An Efficient Retinal Vessel Segmentation Framework With Adaptive Enhancement and Depthwise Separable Convolutions

Autoren: Shuting Chen Chengxi Hong Hong Jia

Quelle: IEEE Access, Vol 13, Pp 122207-122223 (2025)

Schlagwörter: retinal vessel segmentation, morphology-aware processing, adaptive thresholding, depth-separable convolutions, Electrical engineering. Electronics. Nuclear engineering, Medical image segmentation, adaptive data augmentation, TK1-9971

Zugangs-URL: https://doaj.org/article/a7322176686c4bb1a02cb57667dc3160

Building efficient CNNs using Depthwise Convolutional Eigen-Filters (DeCEF)

Autoren: Yu, Yinan Scheidegger, Samuel McKelvey, Tomas

Quelle: Neurocomputing. 609

Schlagwörter: Convolutional neural network, Efficient network, Deep learning, Network complexity, Low rank approximation, Subspace method

Dateibeschreibung: electronic

Zugangs-URL: https://research.chalmers.se/publication/542743
https://research.chalmers.se/publication/542728
https://research.chalmers.se/publication/542743/file/542743_Fulltext.pdf

Fusing Depthwise and Pointwise Convolutions for Efficient Inference on GPUs

Autoren: Mohammad Qararyah, Fareed Azhar, Muhammad Waqar Maleki, Mohammad Ali et al.

Quelle: Very Efficient Deep Learning in IOT (VEDLIoT) EPI SGA2 53rd International Conference on Parallel Processing, ICPP 2024, Gotland, Sweden ACM International Conference Proceeding Series. :58-67

Schlagwörter: layer fusion, depthwise convolution, CNN, vision transformer, pointwise convolution, GPU

Dateibeschreibung: electronic

Zugangs-URL: https://research.chalmers.se/publication/542723
https://research.chalmers.se/publication/543131
https://research.chalmers.se/publication/543131/file/543131_Fulltext.pdf

Towards Lightweight Hyperspectral Image Super-Resolution with Depthwise Separable Dilated Convolutional Network

Autoren: Muhammad, Usman Laaksonen, Jorma Mihaylova, Lyudmila

Quelle: 2025 IEEE Statistical Signal Processing Workshop (SSP). :91-95

Schlagwörter: FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Image and Video Processing

Zugangs-URL: http://arxiv.org/abs/2505.00374

Pengenalan Gambar Braille Menggunakan Depthwise Separable Convolutional Neural Network

Autoren: Kevin Maulana Afriyanto Abas Setiawan

Quelle: Techno.Com, Vol 24, Iss 2, Pp 355-364 (2025)

Schlagwörter: Information technology, T58.5-58.64

Zugangs-URL: https://doaj.org/article/563021c337974cf4b7c81905356a9354

Advanced Multi Class Cyber Security Attack Classification in IoT Based Wireless Sensor Networks Using Context Aware Depthwise Separable Convolutional Neural Network

Autoren: Bangar Raju Cherukuri

Quelle: Journal of Machine and Computing. :814-830

Area-Specific Convolutional Neural Network with Depthwise Convolution for Super-Resolution

Autoren: Seeun Kim Hyunji Lee Kyujoong Lee

Quelle: Journal of Korea Multimedia Society. 28:733-740

Spectral-aware CNN with learnable biorthogonal units and depthwise convolutions for multi-class blood cell classification

Autoren: Sannasi Chakravarthy SR Harikumar Rajaguru Rajesh Kumar Dhanaraj et al.

Quelle: MethodsX, Vol 15, Iss , Pp 103685- (2025)

Schlagwörter: Blood cell classification, Deep learning, Wavelet CNN, Spectral-aware downsampling, Depthwise separable convolution, Science

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2215016125005291; https://doaj.org/toc/2215-0161

Zugangs-URL: https://doaj.org/article/ca21896ca4fc469aad990eaabadb1352

MSDANet: A Multiscale Dual-Channel Spatial Attention Network with Depthwise Separable Convolution for Hyperspectral Image Classification

Autoren: Jianshang Liao Liguo Wang

Quelle: Canadian Journal of Remote Sensing, Vol 51, Iss 1 (2025)

Schlagwörter: hyperspectral image classification, deep learning, multiscale features, channel spatial attention, depthwise separable convolution, Environmental sciences, GE1-350, Technology

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/1712-7971

Zugangs-URL: https://doaj.org/article/40255d427497417c8b228f322ff579e8

Sparse Weight Depthwise Separable with Convolution Neural Network for Video Encoding

Quelle: International Journal of Intelligent Engineering and Systems. 18:72-82

Design and Implementation of a Lightweight GoogLeNet Image Classification Model Integrating Depthwise Separable Convolutions

Autoren: Zhenlin Liu Liwei Liu Wei Huang et al.

Quelle: 2025 10th International Conference on Electronic Technology and Information Science (ICETIS). :267-271

DSC-SparseFormer: a lightweight framework for bearing fault diagnosis based on depthwise separable convolution and sparse attention mechanism

Autoren: Ziyao Geng Shihua Zhou Zhaohui Ren et al.

Quelle: Nondestructive Testing and Evaluation. :1-27