One-shot learning-based animal video segmentation
Deep learning-based video segmentation methods can offer a good performance after being
trained on the large-scale pixel labeled datasets. However, a pixel-wise manual labeling of …
trained on the large-scale pixel labeled datasets. However, a pixel-wise manual labeling of …
Multimodel feature reinforcement framework using Moore–Penrose inverse for big data analysis
Fully connected representation learning (FCRL) is one of the widely used network structures
in multimodel image classification frameworks. However, most FCRL-based structures, for …
in multimodel image classification frameworks. However, most FCRL-based structures, for …
Multimodal moore–penrose inverse-based recomputation framework for big data analysis
Most multilayer Moore–Penrose inverse (MPI)-based neural networks, such as deep random
vector functional link (RVFL), are structured with two separate stages: unsupervised feature …
vector functional link (RVFL), are structured with two separate stages: unsupervised feature …
Blockwise recursive Moore–Penrose inverse for network learning
Training neural networks with the Moore–Penrose (MP) inverse has recently gained
attention in view of its noniterative training nature. However, a significant drawback of …
attention in view of its noniterative training nature. However, a significant drawback of …
IMU data and GPS position information direct fusion based on LSTM
X Guang, Y Gao, P Liu, G Li - Sensors, 2021 - mdpi.com
In recent years, the application of deep learning to the inertial navigation field has brought
new vitality to inertial navigation technology. In this study, we propose a method using long …
new vitality to inertial navigation technology. In this study, we propose a method using long …
Fast transfer learning method using random layer freezing and feature refinement strategy
Recently, Moore-Penrose inverse (MPI)-based parameter fine-tuning of fully connected (FC)
layers in pretrained deep convolutional neural networks (DCNNs) has emerged within the …
layers in pretrained deep convolutional neural networks (DCNNs) has emerged within the …
HKPM: A hierarchical key-area perception model for HFSWR maritime surveillance
High-frequency surface wave radar (HFSWR) has become the cornerstone of maritime
surveillance because of its low-cost maintenance and coverage of wide area. However …
surveillance because of its low-cost maintenance and coverage of wide area. However …
Semisupervised manifold regularization via a subnetwork-based representation learning model
Semisupervised classification with a few labeled training samples is a challenging task in
the area of data mining. Moore–Penrose inverse (MPI)-based manifold regularization (MR) …
the area of data mining. Moore–Penrose inverse (MPI)-based manifold regularization (MR) …
A fast routing capsule network with improved dense blocks
Routing algorithm in most existing Capsule Networks (CapsNets) is always a challenging
problem due to its heavy computational burden. To address this issue, we propose a “fast …
problem due to its heavy computational burden. To address this issue, we propose a “fast …
Filter pruning by quantifying feature similarity and entropy of feature maps
Y Liu, K Fan, D Wu, W Zhou - Neurocomputing, 2023 - Elsevier
Filter pruning can effectively reduce the time cost and computing resources of convolutional
neural networks (CNNs), and is well applied to lightweight edge devices. However, most of …
neural networks (CNNs), and is well applied to lightweight edge devices. However, most of …