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761
Self-Supervised Representation Learning for Remote Sensing Image Change Detection Based on Temporal Prediction
Published 2020-06-01“…Recently, deep learning techniques have reported compelling performance on robust feature learning. However, generating accurate semantic supervision that reveals real change information in satellite images still remains challenging, especially for manual annotation. …”
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762
ECNN: Intelligent Fault Diagnosis Method Using Efficient Convolutional Neural Network
Published 2022-09-01“…With outstanding deep feature learning and nonlinear classification abilities, Convolutional Neural Networks (CNN) have been gradually applied to deal with various fault diagnosis tasks. …”
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763
A Change Detection Method Based on Multi-Scale Adaptive Convolution Kernel Network and Multimodal Conditional Random Field for Multi-Temporal Multispectral Images
Published 2022-10-01“…Multispectral images usually contain many complex scenes, such as ground objects with diverse scales and proportions, so the change detection task expects the feature extractor is superior in adaptive multi-scale feature learning. To address the above-mentioned problems, a multispectral image change detection method based on multi-scale adaptive kernel network and multimodal conditional random field (MSAK-Net-MCRF) is proposed. …”
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764
Divide-and-Attention Network for HE-Stained Pathological Image Classification
Published 2022-06-01“…With such decomposed pathological images, the DANet first performs feature learning independently in each branch, and then focuses on the most important feature representation through the branch selection attention module. …”
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765
A New Road Damage Detection Baseline with Attention Learning
Published 2022-07-01“…Although some public datasets provide a database for the development of ADRD, their amounts of data and the standard of classification cannot meet network training and feature learning. With the aim of solving this problem, this work publishes a new road damage dataset named CNRDD, which is labeled according to the latest evaluation standard for highway technical conditions in China (JTG5210-2018). …”
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766
CM-NET: Cross-Modal Learning Network for CSI-Based Indoor People Counting in Internet of Things
Published 2022-12-01“…Owing to the complexity of human location-random environments, the transformer model cannot extract characteristics describing the number of people. To enhance the feature learning capability of the transformer model, CM-NET takes the feature knowledge learned by the image-based people counting model to supervise the learning process. …”
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767
Single-Trial Recognition of Video Gamer’s Expertise from Brain Haemodynamic and Facial Emotion Responses
Published 2021-01-01“…From the data collected, i.e., gamer’s fNIRS data in combination with emotional state estimation from gamer’s facial expressions, the expertise level of the gamers has been decoded per trial in a multi-modal framework comprising of unsupervised deep feature learning and classification by state-of-the-art models. …”
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768
Attribute-Guided Multi-Level Attention Network for Fine-Grained Fashion Retrieval
Published 2024-01-01“…This can further alleviate the feature gap problem by perturbing object-centric feature learning. Moreover, we propose an improved attribute-guided attention module for extracting more accurate attribute-specific representations. …”
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769
Spatial-temporal transformer network for multi-year ENSO prediction
Published 2023-03-01“…To solve these problem, we propose a spatio-temporal transformer network to model the inherent characteristics of the sea surface temperature anomaly map and heat content anomaly map along with the changes in space and time by designing an effective attention mechanism, and innovatively incorporate temporal index into the feature learning procedure to model the influence of seasonal variation on the prediction of the ENSO phenomenon. …”
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770
Composition of Hybrid Deep Learning Model and Feature Optimization for Intrusion Detection System
Published 2023-01-01“…Current approaches are aligned towards deep learning (DL) methods for IDSs, but the success of the DL mechanism depends on the feature learning process, which is an open challenge. Thus, in this paper, the authors propose a technique which combines both CNN, and GRU, where different CNN–GRU combination sequences are presented to optimize the network parameters. …”
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771
Multimodal EEG Emotion Recognition Based on the Attention Recurrent Graph Convolutional Network
Published 2022-11-01“…This proposed method performs feature learning in three dimensions of time, space, and frequency by excavating the complementary relationship of different modal data so that the learned deep emotion-related features are more discriminative. …”
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772
Semi-Supervised Seizure Prediction Model Combining Generative Adversarial Networks and Long Short-Term Memory Networks
Published 2023-10-01“…Specifically, we utilize the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) as the feature learning model, using the Earth Mover’s distance and gradient penalty to guide the unsupervised training process and train a high-order feature extractor. …”
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773
Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach
Published 2016-05-01“…The framework enables both representational feature learning and fault classification. The multi-layered ELM based representational learning covers functions including data preprocessing, feature extraction and dimension reduction. …”
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774
Hybrid Dense Network With Attention Mechanism for Hyperspectral Image Classification
Published 2022-01-01“…However, fixed kernel sizes make traditional CNN too specific, neither flexible nor conducive to feature learning, thus impacting on the classification accuracy. …”
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775
Ensemble streamflow forecasting based on variational mode decomposition and long short term memory
Published 2022-01-01“…VMD-LSTM-GBRT was compared with respect to three aspects: (1) feature extraction algorithm; ensemble empirical mode decomposition (EEMD) was used. (2) Feature learning techniques; deep neural networks (DNNs) and support vector machines for regression (SVRs) were exploited. (3) Ensemble strategy; the summation strategy was used. …”
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776
Ship Infrared Automatic Target Recognition Based on Bipartite Graph Recommendation: A Model-Matching Method
Published 2024-01-01“…This method establishes evaluation criteria based on recognition accuracy and feature learning credibility, uncovering the underlying connections between IR attributes of ships and candidate models. …”
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777
Deforestation detection using deep learning-based semantic segmentation techniques: a systematic review
Published 2024-02-01“…The review underscores the pivotal role of satellite imagery in capturing spatial information and highlights the strengths of various deep learning architectures in deforestation analysis. Multiscale feature learning and fusion emerge as critical strategies enabling deep networks to comprehend contextual nuances across various scales. …”
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778
GPR Data Reconstruction Using Residual Feature Distillation Block U-Net
Published 2023-01-01“…To be specific, by employing the information distillation network based on the multiple feature extraction connections, RFDB is capable of utilizing the adequate residual information of each layer for feature learning. Moreover, a skip connection is additional patched on the residual units to properly compensate the missing features in the convolution process. …”
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779
End-to-End Visual Domain Adaptation Network for Cross-Domain 3D CPS Data Retrieval
Published 2019-01-01“…Specifically, an center-based discriminative feature learning method enables the domain invariant features with better intra-class compactness and inter-class separability. …”
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780
COVID-19 Infection Segmentation and Severity Assessment Using a Self-Supervised Learning Approach
Published 2022-07-01“…For COVID-19 severity classification, the proposed method outperformed other unsupervised feature learning methods by about 7.16% in accuracy. For segmentation, when the amount of labeled data was 100%, the Dice value of the proposed method was 5.58% higher than that of U-Net.; in 70% of the cases, our method was 8.02% higher than U-Net; in 30% of the cases, our method was 11.88% higher than U-Net; and in 10% of the cases, our method was 16.88% higher than U-Net. …”
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