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1001
3D Convolutional Neural Network with Dimension Reduction and Metric Learning for Crop Yield Prediction Based on Remote Sensing Data
Published 2023-12-01“…In addition, regions with similar crop yields should have similar features learned by the network. Thus, metric learning and multitask learning are used to learn more discriminative features. …”
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1002
An Improved Modulation Recognition Algorithm Based on Fine-Tuning and Feature Re-Extraction
Published 2023-05-01“…Finally, on the basis of the features learned by the network, deeper features with enhanced discriminability for confused modulation types are obtained using feature re-extraction. …”
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1003
UAV Detection with Transfer Learning from Simulated Data of Laser Active Imaging
Published 2021-06-01“…The experimental results show that the performance of the proposed method is greatly improved compared to the model not trained on the simulated dataset, which verifies the transferability of features learned from the simulated data, the effectiveness of the proposed simulation method, and the feasibility of our solution for UAV detection in the laser active imaging domain. …”
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1004
Hyperspectral Image Super-Resolution under the Guidance of Deep Gradient Information
Published 2021-06-01“…The residual dense network is used to fully exploit the hierarchical features learned from all the convolutional layers. Meanwhile, the gradient detail is extracted via a residual network (ResNet), which is further utilized to guide the super-resolution process. …”
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1005
Mapping the tidal marshes of coastal Virginia: a hierarchical transfer learning approach
Published 2024-12-01“…We illustrate that by leveraging features learned from data abundant regions and small quantities of high-quality training data collected from the target region, an accuracy as high as 88% can be achieved in the classification of marsh types, specifically high marsh and low marsh, at a spatial resolution of 0.6 m. …”
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1006
Adaptive Feature Pyramid Network to Predict Crisp Boundaries via NMS Layer and ODS F-Measure Loss Function
Published 2022-01-01“…Furthermore, to enrich multi-scale features learned by AFPN, we introduce a pyramid context module (PCM) that includes dilated convolution to extract multi-scale features. …”
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1007
A Hybrid Deep Learning Model for Protein–Protein Interactions Extraction from Biomedical Literature
Published 2020-04-01“…The visualization and comparison of the hidden features learned by different DL models further confirmed the effectiveness of the proposed model.…”
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1008
Deep learning for diagnosis of acute promyelocytic leukemia via recognition of genomically imprinted morphologic features
Published 2021-05-01“…We hypothesized that genomic imprinting of morphologic features learned by deep learning pattern recognition would have greater discriminatory power and consistency compared to humans, thereby facilitating identification of t(15;17) positive APL. …”
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1009
A novel decoder based on Bayesian rules for task‐driven object segmentation
Published 2023-02-01“…What's more, a Bayesian rule is established in the decoder, in which the control signal is set as the prior, and the latent features learned in encoder is transferred to the corresponding layer of decoder as observation, thus the posterior probability of each object with respect to the specific‐class can be calculated, and the objects belonging to this class can be segmented. …”
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1010
EPick: Attention-based multi-scale UNet for earthquake detection and seismic phase picking
Published 2022-11-01“…By incorporating the attention mechanism into UNet, EPick can address different levels of deep features, and the decoder can take full advantage of the multi-scale features learned from the encoder part to achieve precise phase picking. …”
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1011
Feature boosting network for 3D pose estimation
Published 2022“…In this method, the features learned by the convolutional layers are boosted with a new long short-term dependence-aware (LSTD) module, which enables the intermediate convolutional feature maps to perceive the graphical long short-term dependency among different hand (or body) parts using the designed Graphical ConvLSTM. …”
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Journal Article -
1012
Can We Survive without Labelled Data in NLP? Transfer Learning for Open Information Extraction
Published 2020-08-01“…In this paper, we relied upon features learned to generate relation triples from the open information extraction (OIE) task. …”
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1013
Analysis and positioning of geographic tourism resources based on image processing method with Ra-CGAN modeling
Published 2022-09-01“…Finally, through adversarial training between G and D with conditional constraints, we enabled high-order data distribution features learning to improve the boundary accuracy and smoothness of the segmentation results. …”
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1014
A Novel Methodology for Human Plasma Protein Binding: Prediction, Validation, and Applicability Domain
Published 2022-10-01“…Then, the latest features learned from the CNN layers were flattened out and passed through an FFNN to make predictions. …”
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1015
How does interactive virtual reality enhance learning outcomes via emotional experiences? A structural equation modeling approach
Published 2023-01-01“…Based on media technology models and the control value theory of achievement emotions (CVTAE), this study uses structural equation modeling (SEM) to investigate the correlations among the internal elements of IVR technology features, learning experiences, and learning outcomes. It also emphasizes the role played by emotional experience in this context.MethodsThe sample referenced by this study consisted of 480 college students (193 males) who were simultaneously engaged in guided inquiry and learning in an IVR-based COVID-19 pandemic science museum in groups of 10.ResultsThe findings suggest that presence and perceived enjoyment have a key mediating effect on the relationship between virtual reality (VR) features and perceived learning outcomes in an IVR-based learning simulation. …”
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1016
A Hybrid Visual Tracking Algorithm Based on SOM Network and Correlation Filter
Published 2021-04-01“…Objects in different videos or images often have completely different appearance, therefore, the self-organization mapping neural network with the characteristics of signal processing mechanism of human brain neurons is used to perform adaptive and unsupervised features learning. A reliable method of robust target tracking is proposed, based on multiple adaptive correlation filters with a memory function of target appearance at the same time. …”
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1017
Sign Language Recognition Using Two-Stream Convolutional Neural Networks with Wi-Fi Signals
Published 2020-12-01“…After the two stream networks are fused, an attention mechanism is applied to select the important features learned by the two-stream networks. Our method has been validated by the public dataset SignFi and adopted five-fold cross-validation. …”
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1018
Explaining Neural Networks Using Attentive Knowledge Distillation
Published 2021-02-01“…Experiments demonstrated that the saliency maps are the result of spatially attentive features learned from the distillation. Thus, they are useful for fine-grained classification tasks. …”
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1019
Animal classification using facial images with score‐level fusion
Published 2018-08-01“…This method utilises a score‐level fusion of two different approaches; one uses CNN which can automatically extract features, learn and classify them; and the other one uses kernel Fisher analysis (KFA) for its feature extraction phase. …”
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1020
Forest Fire Smoke Detection Based on Deep Learning Approaches and Unmanned Aerial Vehicle Images
Published 2023-06-01“…A BiFPN was used to accelerate multi-scale feature fusion and acquire more specific features. Learning weights were introduced in the BiFPN so that the network can prioritize the most significantly affecting characteristic mapping of the result characteristics. …”
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