3D Convolutional Neural Network with Dimension Reduction and Metric Learning for Crop Yield Prediction Based on Remote Sensing Data by Ning Wang, Zhong Ma, Pengcheng Huo, Xi Liu, Zhao He, Kedi Lu

“…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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An Improved Modulation Recognition Algorithm Based on Fine-Tuning and Feature Re-Extraction by Yibing Wang, Liang Zhou, Zhutian Yang, Longwen Wu, Zhendong Yin, Yaqin Zhao, Zhilu Wu

“…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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UAV Detection with Transfer Learning from Simulated Data of Laser Active Imaging by Shao Zhang, Guoqing Yang, Tao Sun, Kunyang Du, Jin Guo

“…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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Hyperspectral Image Super-Resolution under the Guidance of Deep Gradient Information by Minghua Zhao, Jiawei Ning, Jing Hu, Tingting Li

“…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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Mapping the tidal marshes of coastal Virginia: a hierarchical transfer learning approach by Zhonghui Lv, Karinna Nunez, Ethan Brewer, Dan Runfola

“…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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Adaptive Feature Pyramid Network to Predict Crisp Boundaries via NMS Layer and ODS F-Measure Loss Function by Gang Sun, Hancheng Yu, Xiangtao Jiang, Mingkui Feng

“…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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A Hybrid Deep Learning Model for Protein–Protein Interactions Extraction from Biomedical Literature by Changqin Quan, Zhiwei Luo, Song Wang

“…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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Deep learning for diagnosis of acute promyelocytic leukemia via recognition of genomically imprinted morphologic features by John-William Sidhom, Ingharan J. Siddarthan, Bo-Shiun Lai, Adam Luo, Bryan C. Hambley, Jennifer Bynum, Amy S. Duffield, Michael B. Streiff, Alison R. Moliterno, Philip Imus, Christian B. Gocke, Lukasz P. Gondek, Amy E. DeZern, Alexander S. Baras, Thomas Kickler, Mark J. Levis, Eugene Shenderov

“…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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A novel decoder based on Bayesian rules for task‐driven object segmentation by Yuxiang Cai, Yuanlong Yu, Weijie Jiang, Rong Chen, Weitao Zheng, Xi Wu, Renjie Su

“…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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EPick: Attention-based multi-scale UNet for earthquake detection and seismic phase picking by Wei Li, Megha Chakraborty, Megha Chakraborty, Darius Fenner, Darius Fenner, Johannes Faber, Johannes Faber, Kai Zhou, Kai Zhou, Kai Zhou, Georg Rümpker, Georg Rümpker, Horst Stöcker, Horst Stöcker, Horst Stöcker, Horst Stöcker, Nishtha Srivastava, Nishtha Srivastava

“…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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Feature boosting network for 3D pose estimation by Liu, Jun, Ding, Henghui, Shahroudy, Amir, Duan, Ling-Yu, Jiang, Xudong, Wang, Gang, Kot, Alex C.

“…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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Can We Survive without Labelled Data in NLP? Transfer Learning for Open Information Extraction by Injy Sarhan, Marco Spruit

“…In this paper, we relied upon features learned to generate relation triples from the open information extraction (OIE) task. …”
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Analysis and positioning of geographic tourism resources based on image processing method with Ra-CGAN modeling by Xiuxia Li

“…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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A Novel Methodology for Human Plasma Protein Binding: Prediction, Validation, and Applicability Domain by Affaf Khaouane, Samira Ferhat, Salah Hanini

“…Then, the latest features learned from the CNN layers were flattened out and passed through an FFNN to make predictions. …”
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How does interactive virtual reality enhance learning outcomes via emotional experiences? A structural equation modeling approach by Hairu Yang, Minghan Cai, Yongfeng Diao, Rui Liu, Ling Liu, Qianchen Xiang

“…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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A Hybrid Visual Tracking Algorithm Based on SOM Network and Correlation Filter by Yuanping Zhang, Xiumei Huang, Ming Yang

“…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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Sign Language Recognition Using Two-Stream Convolutional Neural Networks with Wi-Fi Signals by Chien-Cheng Lee, Zhongjian Gao

“…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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Explaining Neural Networks Using Attentive Knowledge Distillation by Hyeonseok Lee, Sungchan Kim

“…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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Animal classification using facial images with score‐level fusion by Shahram Taheri, Önsen Toygar

“…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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Forest Fire Smoke Detection Based on Deep Learning Approaches and Unmanned Aerial Vehicle Images by Soon-Young Kim, Azamjon Muminov

“…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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