Spatiotemporal Feature Learning Based Hour-Ahead Load Forecasting for Energy Internet by Liufeng Du, Linghua Zhang, Xu Wang

“…Third, we develop a deep forecasting framework (called the 3D CNN-GRU) featuring a feature learning module followed by a regression module, in which the 3D convolutional neural network (3D CNN) is used to extract the desired feature sequences with time attributes, while the gated recurrent unit (GRU) is responsible for mapping the sequences to the forecast values. …”
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Image completion algorithm of anthurium spathes based on multi-scale feature learning by Hongyu Wei, Jiahui Li, Wenyue Chen, Xuan Chu, Hongli Liu, Yinghui Mu, Zhiyu Ma

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Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors by Frédéric Li, Kimiaki Shirahama, Muhammad Adeel Nisar, Lukas Köping, Marcin Grzegorzek

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Automatic Dataset Expansion With Structured Feature Learning for Human Lying Pose Detection by Daoxun Xia, Lingjin Zhao, Fang Guo, Xi Chen

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Jointly Feature Learning and Selection for Robust Tracking via a Gating Mechanism. by Bineng Zhong, Jun Zhang, Pengfei Wang, Jixiang Du, Duansheng Chen

“…To achieve effective visual tracking, a robust feature representation composed of two separate components (i.e., feature learning and selection) for an object is one of the key issues. …”
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Multiple Feature Learning Based on Edge-Preserving Features for Hyperspectral Image Classification by Wei Tian, Lizhong Xu, Zhe Chen, Aiye Shi

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Unsupervised Deep Feature Learning With Iteratively Refined Pseudo Classes for Scene Representation by Zhiqiang Gong, Ping Zhong, Weidong Hu

“…To overcome this problem, this work develops a novel unsupervised deep feature learning framework with iteratively refined pseudo-classes for remote sensing scene representation. …”
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Deep learning model construction for a semi-supervised classification with feature learning by Sridhar Mandapati, Seifedine Kadry, R. Lakshmana Kumar, Krongkarn Sutham, Orawit Thinnukool

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INDOOR SCENE REGISTRATION BASED ON KEY POINTS SAMPLING AND HIERARCHICAL FEATURE LEARNING by M. Ai, M. Ai, C. Liu, H. Shen, F. Cheng

“…Second, hierarchical feature learning network is trained to describe the local group as feature descriptors. …”
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Graph Signal Smoothness Based Feature Learning of Brain Functional Networks in Schizophrenia by Xiaoying Song, Li Chai

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Person Re-identification by Region Correlated Deep Feature Learning with Multiple Granularities by DONG Hu-sheng, ZHONG Shan, YANG Yuan-feng, SUN Xun, GONG Sheng-rong

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Adversarial Remote Sensing Scene Classification Based on Lie Group Feature Learning by Chengjun Xu, Jingqian Shu, Guobin Zhu

“…To address this problem, a novel supervised adversarial Lie Group feature learning network is proposed. In the case of limited data samples, the model can effectively generate data samples with category information. …”
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Supervised Extreme Learning Machine-Based Auto-Encoder for Discriminative Feature Learning by Jie Du, Chi-Man Vong, Chuangquan Chen, Peng Liu, Zhenbao Liu

“…In this paper, a Supervised Extreme Learning Machine-based Auto-Encoder (SELM-AE) is proposed for discriminative Feature Learning. Different from traditional ELM-AE (designed based on data information X only), SELM-AE is designed based on both data information X and label information T. …”
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SARPointNet: An Automated Feature Learning Framework for Spaceborne SAR Image Registration by Xin Li, Taoyang Wang, Hao Cui, Guo Zhang, Qian Cheng, Tiancheng Dong, Boyang Jiang

“…This article innovatively proposes a spaceborne SAR image feature learning framework to realize automatic sample generation and model training. …”
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Frequency Feature Learning from Vibration Information of GIS for Mechanical Fault Detection by Yang Yuan, Suliang Ma, Jianwen Wu, Bowen Jia, Weixin Li, Xiaowu Luo

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Emotion Classification Based on Transformer and CNN for EEG Spatial–Temporal Feature Learning by Xiuzhen Yao, Tianwen Li, Peng Ding, Fan Wang, Lei Zhao, Anmin Gong, Wenya Nan, Yunfa Fu

“…To address these challenges, the present study proposes a novel model based on transformer and convolutional neural networks (TCNN) for EEG spatial–temporal (EEG ST) feature learning to automatic emotion classification. Methods: The proposed EEG ST-TCNN model utilizes position encoding (PE) and multi-head attention to perceive channel positions and timing information in EEG signals. …”
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Unsupervised SAR Imagery Feature Learning with Median Filter-Based Loss Value by Krzysztof Gromada

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Privacy-Preserving Face Recognition Method Based on Randomization and Local Feature Learning by Yanhua Huang, Zhendong Wu, Juan Chen, Hui Xiang

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Hyperspectral Image Classification Using Convolutional Neural Networks and Multiple Feature Learning by Qishuo Gao, Samsung Lim, Xiuping Jia

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Structured Cluster Detection from Local Feature Learning for Text Region Extraction by Huei-Yung Lin, Chin-Yu Hsu

“…Different from the existing methods, our approach takes the application-specific reference images for feature learning and extraction. It is able to identify text clusters under the sparsity of feature points derived from the characters. …”
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