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Published in 2017 at "Multimedia Systems"
DOI: 10.1007/s00530-017-0583-4
Abstract: Background interference, which arises from complex environment, is a critical problem for a robust person re-identification (re-ID) system. The background noise may significantly compromise the feature learning and matching process. To reduce the background interference,…
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Keywords:
saliency image;
image;
cnn architecture;
person ... See more keywords
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Published in 2021 at "IEEE Access"
DOI: 10.1109/access.2021.3054117
Abstract: Convolutional Neural Networks (CNNs) models achieve a dominant performance on immense domains. There are CNNs that come in numerous topologies of different sizes. This field’s challenge is to design the right CNN architecture for a…
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Keywords:
design approach;
based ensemble;
architecture;
design ... See more keywords
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Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3204758
Abstract: Brain–computer interface (BCI) is a technology that allows users to control computers by reflecting their intentions. Electroencephalogram (EEG)–based BCI has been developed because of its potential, however, its decoding performance is still insufficient to apply…
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Keywords:
decoding performance;
performance;
cnn architecture;
rethinking cnn ... See more keywords
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Published in 2019 at "IEEE Journal on Emerging and Selected Topics in Circuits and Systems"
DOI: 10.1109/jetcas.2019.2952137
Abstract: This paper presents a novel stacking and multi-level indexing scheme for convolutional neural networks (CNNs) used in energy-limited edge-level systems. Basically, the proposed scheme offers multiple accuracy modes by adopting a structured weight pruning method…
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Keywords:
edge level;
structured weight;
cnn architecture;
cnn ... See more keywords
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Published in 2021 at "IEEE Communications Letters"
DOI: 10.1109/lcomm.2021.3091841
Abstract: Device activity detection has been extensively investigated for grant-free massive machine-type communications. Instead of using deep Multi-Layer Perception (MLP) networks, this letter proposes a novel convolutional neural network (CNN) architecture for learning device activity from…
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Keywords:
architecture learning;
device activity;
cnn architecture;
device ... See more keywords
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Published in 2023 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2023.3237962
Abstract: Inspired by the global-local information processing mechanism in the human visual system, we propose a novel convolutional neural network (CNN) architecture named cognition-inspired network (CogNet) that consists of a global pathway, a local pathway, and…
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Keywords:
cnn architecture;
cnn;
dual pathway;
image ... See more keywords
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Published in 2019 at "PLoS ONE"
DOI: 10.1371/journal.pone.0223315
Abstract: Background Robust Artificial-neural-networks for k-space Interpolation (RAKI) is a recently proposed deep-learning-based reconstruction algorithm for parallel imaging. Its main premise is to perform k-space interpolation using convolutional neural networks (CNNs) trained on subject-specific autocalibration signal…
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Keywords:
cnn architecture;
neural networks;
space interpolation;
raki ... See more keywords