Results 31 to 40 of about 4,886 (174)

Lightweight Image Super-Resolution Reconstruction Based on Depthwise Separable Convolution [PDF]

Jisuanji gongcheng, 2022
Image super-resolution reconstruction aims to reconstruct a high-resolution image close to the real image according to the low-resolution image.The existing image super-resolution reconstruction methods based on the Convolutional Neural Network(CNN ...
LIU Cong, QU Dan, SI Nianwen, WEI Ziwei
doaj   +1 more source

A lightweight attention deep learning method for human-vehicle recognition based on wireless sensing technology

Frontiers in Neuroscience, 2023
Wireless sensing-based human-vehicle recognition (WiHVR) methods have become a hot spot for research due to its non-invasiveness and cost-effective advantages.
Mingxin Song, Mingxin Song, Rensheng Zhu, Xinquan Chen, Xinquan Chen, Chunlei Zheng, Liangliang Lou   +6 more
doaj   +1 more source

A fast block partitioning algorithm for VVC intra coding based on depthwise separable convolution

Dianxin kexue, 2023
The joint video exploration team (JVET) proposed versatile video coding (VVC) as a new video coding standard, and its quadtree plus multi-type tree (QTMTT) partition structure brings effective coding performance improvements.However, it brings about a ...
Zhen YE, Guoxiang WANG, Junfeng SONG, Haokun LIU, Tiansong LI   +4 more
doaj   +2 more sources

Few-Shot Learning-Based Light-Weight WDCNN Model for Bearing Fault Diagnosis in Siamese Network

Sensors, 2023
In this study, bearing fault diagnosis is performed with a small amount of data through few-shot learning. Recently, a fault diagnosis method based on deep learning has achieved promising results. Most studies required numerous training samples for fault
Daehwan Lee, Jongpil Jeong
doaj   +1 more source

A multi-scale gated multi-head attention depthwise separable CNN model for recognizing COVID-19

Scientific Reports, 2021
Coronavirus 2019 (COVID-19) is a new acute respiratory disease that has spread rapidly throughout the world. In this paper, a lightweight convolutional neural network (CNN) model named multi-scale gated multi-head attention depthwise separable CNN ...
Geng Hong, Xiaoyan Chen, Jianyong Chen, Miao Zhang, Yumeng Ren, Xinyu Zhang   +5 more
doaj   +1 more source

Depthwise Separable Convolutions with Deep Residual Convolutions

CoRR
The recent advancement of edge computing enables researchers to optimize various deep learning architectures to employ them in edge devices. In this study, we aim to optimize Xception architecture which is one of the most popular deep learning algorithms for computer vision applications.
Md. Arid Hasan, Krishno Dey
openaire   +3 more sources

Optimizing depthwise separable convolution on DCU

CCF Transactions on High Performance Computing
Abstract The integration of Large Language Models (LLMs) with Convolutional Neural Networks (CNNs) is significantly advancing the development of large models. However, the computational cost of large models is high, necessitating optimization for greater efficiency. One effective way to optimize the CNN is the use
Zheng Liu, Meng Hao 0002, Weizhe Zhang, Gangzhao Lu, Xueyang Tian, Siyu Yang 0002, Mingdong Xie, Jie Dai, Chenyu Yuan, Desheng Wang 0002, Hongwei Yang   +10 more
openaire   +2 more sources

An multi-scale learning network with depthwise separable convolutions

IPSJ Transactions on Computer Vision and Applications, 2018
Abstract We present a simple multi-scale learning network for image classification that is inspired by the MobileNet. The proposed method has two advantages: (1) It uses the multi-scale block with depthwise separable convolutions, which forms multiple sub-networks by increasing the width of the network while keeping the computational ...
Gaihua Wang, Guoliang Yuan 0003, Tao Li, Meng Lv   +3 more
openaire   +2 more sources

SpaCut: Transcript‐Aware Morphology Fusion for Robust Cell Segmentation in Subcellular Spatial Transcriptomics

Advanced Science, EarlyView.
Subcellular spatial transcriptomics enables high‐resolution mapping of gene expression within tissues, but accurate cell segmentation is essential for assigning transcripts to individual cells. We present SpaCut, a deep learning framework that integrates transcriptomic features with nucleus‐guided morphological information to precisely delineate cell ...
Yongheng Li, Fangfang Zhu, Wenwen Min
wiley   +1 more source

Network Decoupling: From Regular to Depthwise Separable Convolutions

CoRR, 2018
Depthwise separable convolution has shown great efficiency in network design, but requires time-consuming training procedure with full training-set available. This paper first analyzes the mathematical relationship between regular convolutions and depthwise separable convolutions, and proves that the former one could be approximated with the latter one
Jianbo Guo, Yuxi Li 0009, Weiyao Lin, Yurong Chen 0001, Jianguo Li   +4 more
openaire   +3 more sources