Results 81 to 90 of about 2,516,559 (186)

An Adaptive Feature Learning Model for Sequential Radar High Resolution Range Profile Recognition

Sensors, 2017
This paper proposes a new feature learning method for the recognition of radar high resolution range profile (HRRP) sequences. HRRPs from a period of continuous changing aspect angles are jointly modeled and discriminated by a single model named the ...
Xuan Peng, Xunzhang Gao, Yifan Zhang, Xiang Li   +3 more
doaj   +1 more source

Learning Relationship-Aware Visual Features

, 2019
Relational reasoning in Computer Vision has recently shown impressive results on visual question answering tasks. On the challenging dataset called CLEVR, the recently proposed Relation Network (RN), a simple plug-and-play module and one of the state-of-the-art approaches, has obtained a very good accuracy (95.5%) answering relational questions.
Messina N, Amato G, Carrara F, Falchi F, Gennaro C   +4 more
openaire   +4 more sources

Deep Feature Learning for Disease Risk Assessment Based on Convolutional Neural Network With Intra-Layer Recurrent Connection by Using Hospital Big Data

IEEE Access, 2018
This paper presents the analysis of real-life medical big data obtained from a hospital in central China from 2013 to 2015 for risk assessment of cerebral infarction disease.
Mohd Usama, Belal Ahmad, Jiafu Wan, M. Shamim Hossain, Mohammed F. Alhamid, M. Anwar Hossain   +5 more
doaj   +1 more source

PIPNet: A Deep Convolutional Neural Network for Multibaseline InSAR Phase Unwrapping Based on Pure Integer Programming

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Multibaseline (MB) phase unwrapping (PU), as the core step in MB InSAR, breaks the limitation of phase continuity assumption. However, it still suffers from insufficient noise robustness and low unwrapping efficiency.
Hui Liu, Ke Zheng, Changwei Miao, Xuemei Liu, Xianlin Liu, Lin Li, Yongguang Zhang, Longhai Xiong   +7 more
doaj   +1 more source

Cause Identification of Electromagnetic Transient Events using Spatiotemporal Feature Learning

, 2019
This paper presents a spatiotemporal unsupervised feature learning method for cause identification of electromagnetic transient events (EMTE) in power grids.
Arghandeh, Reza, Hamidi, Reza Jalilzadeh, Livani, Hanif, Niazazari, Iman   +3 more
core  

Extreme learning machines for feature learning [PDF]

, 2020
Neural Networks (NN) map input data to desired output data in image processing, time series prediction and data analytics. The commonly used variant of NN is Single Layer Feed forward Neural network (SLFN) due to its simple network architecture and universal approximation capability.
openaire   +2 more sources

A Novel Transformer-Based Multiscale Siamese Framework for High-Resolution Remote Sensing Change Detection

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Change detection (CD) in remote sensing based on deep convolutional neural networks and transformers has played a crucial role in surface monitoring and resource development. However, the inadequate extraction of local and global features, along with the
Liangjun Wang, Weitao Chen, Haoyi Wang, Zhengchao Chen   +3 more
doaj   +1 more source

Feature learning and generalization in deep networks with orthogonal weights

Machine Learning: Science and Technology
Fully-connected deep neural networks with weights initialized from independent Gaussian distributions can be tuned to criticality, which prevents the exponential growth or decay of signals propagating through the network.
Hannah Day, Yonatan Kahn, Daniel A Roberts   +2 more
doaj   +1 more source

A Multi-Input Channel U-Net Landslide Detection Method Fusing SAR Multisource Remote Sensing Data

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Accurate and efficient landslide identification is an important basis for landslide disaster prevention and control. Due to the diversity of landslide features, vegetation occlusion, and the complexity of the surrounding surface environment in remote ...
Hesheng Chen, Yi He, Lifeng Zhang, Wang Yang, Yaoxiang Liu, Binghai Gao, Qing Zhang, Jiangang Lu   +7 more
doaj   +1 more source

Video anomaly detection using deep incremental slow feature analysis network

IET Computer Vision, 2016
Existing anomaly detection (AD) approaches rely on various hand‐crafted representations to represent video data and can be costly. The choice or designing of hand‐crafted representation can be difficult when faced with a new dataset without prior ...
Xing Hu, Shiqiang Hu, Yingping Huang, Huanlong Zhang, Hanbing Wu   +4 more
doaj   +1 more source