AUD-MTS: An Abnormal User Detection Approach Based on Power Load Multi-Step Clustering with Multiple Time Scales by Rongheng Lin, Fangchun Yang, Mingyuan Gao, Budan Wu, Yingying Zhao

“…Firstly, we combine RBM (Restricted Boltzmann Machine) hidden feature learning with K-Means clustering to extract typical load patterns in the short-term. …”
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Cross-Modality Person Re-Identification Algorithm Based on Two-Branch Network by Jianfeng Song, Jin Yang, Chenyang Zhang, Kun Xie

“…Firstly, we use infrared image colorization technology to convert infrared images into color images to reduce the differences between modalities and propose a visible-infrared cross-modality person re-identification algorithm based on Two-Branch Network with Double Constraints (VI-TBNDC), which consists of two main components: a two-branch network for feature extraction and a double-constrained identity loss for feature learning. The two-branch network extracts the features of both data sets separately, and the double-constrained identity loss ensures that the learned feature representations are discriminative enough to distinguish different people from two different patterns. …”
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Automatic Transmission Bearing Fault Diagnosis Based on Comprehensive Index Method and Convolutional Neural Network by Guangxin Li, Yong Chen, Wenqing Wang, Yimin Wu, Rui Liu

“…Rolling-element bearing fault diagnosis has some problems in the applied environment, such as low signal-to-noise ratio, weak feature extraction, low efficiency of feature learning and the complex structure of diagnosis models. …”
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Model NOx, SO₂ Emissions Concentration and Thermal Efficiency of CFBB Based on a Hyper-Parameter Self-Optimized Broad Learning System by Yunpeng Ma, Chenheng Xu, Hua Wang, Ran Wang, Shilin Liu, Xiaoying Gu

“…A broad learning system (BLS) is a novel neural network algorithm, which shows good performance in multidimensional feature learning. However, the BLS has several hyper-parameters to be set in a wide range, so that the optimal combination between hyper-parameters is difficult to determine. …”
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Neighbor-Based Label Distribution Learning to Model Label Ambiguity for Aerial Scene Classification by Jianqiao Luo, Yihan Wang, Yang Ou, Biao He, Bailin Li

“…Experiments on the aerial image dataset (AID) and NWPU_RESISC45 (NR) datasets demonstrate that using the label distributions clearly improves the classification performance by assisting feature learning and mitigating over-fitting problems, and our method achieves state-of-the-art performance.…”
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Dual Modality Collaborative Learning for Cross-Source Remote Sensing Retrieval by Jingjing Ma, Duanpeng Shi, Xu Tang, Xiangrong Zhang, Licheng Jiao

“…Finally, to supplement the specific knowledge to the common features, we develop modality transformation and the dual-modality feature learning modules. Their function is to transmit the specific knowledge from different sources mutually and fuse the specific and common features adaptively. …”
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Research on Virus Morphology Recognition Method Based on Enhanced Graph Convolutional Network by Yan HA, Weicheng YUAN, Xiangjie MENG, Junfeng TIAN

“…Methods In this model, Convolutional Neural Network (CNN) was used to extract the local feature information between pixels, and GCN was used for graph feature learning combined with the nearest neighbor relationship between sample features. …”
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Multi-Label Attribute Selection of Arrhythmia for Electrocardiogram Signals with Fusion Learning by Jie Yang, Jinfeng Li, Kun Lan, Anruo Wei, Han Wang, Shigao Huang, Simon Fong

“…The objective is to build a unified system with automatic feature learning which supports effective multi-label classification. …”
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Learning Attention-Aware Interactive Features for Fine-Grained Vegetable and Fruit Classification by Yimin Wang, Zhifeng Xiao, Lingguo Meng

“…Specifically, we employ a region proposal network (RPN) to generate a collection of informative regions and apply a biattention module to learn global and local attentive feature maps, which are fused and fed into an interactive feature learning subnetwork. The novel neural structure is verified through extensive experiments and shows consistent performance improvement in comparison with the SOTA on the VegFru data set, demonstrating its superiority in fine-grained vegetable and fruit recognition. …”
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Development and validation of chest CT-based imaging biomarkers for early stage COVID-19 screening by Xiao-Ping Liu, Xiao-Ping Liu, Xu Yang, Miao Xiong, Xuanyu Mao, Xiaoqing Jin, Zhiqiang Li, Shuang Zhou, Hang Chang

“…Furthermore, unsupervised feature learning led to the discovery of clinical-relevant imaging biomarkers from the vasculature-like signals for accurate and sensitive COVID-19 screening that have been double-blindly validated in an independent hospital (sensitivity: 0.941, specificity: 0.920, AUC: 0.971, accuracy 0.931, F1 score: 0.929). …”
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RPU-PVB: robust object detection based on a unified metric perspective with bilinear interpolation by Hao Yang, Xuewei Wang, Yuling Chen, Hui Dou, Yangwen Zhang

“…We propose the robustness optimization based on uniform metric perspective (RPU) for feature learning of clean and adversarial samples, drawing on the fine-grained idea. …”
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Dynamic Community Detection Method of a Social Network Based on Node Embedding Representation by Bo Zhang, Yifei Mi, Lele Zhang, Yuping Zhang, Maozhen Li, Qianqian Zhai, Meizi Li

“…The node embedding method enables network structure feature learning and representation for social network community detection. …”
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Improving Urban Land Cover/Use Mapping by Integrating A Hybrid Convolutional Neural Network and An Automatic Training Sample Expanding Strategy by Xin Luo, Xiaohua Tong, Zhongwen Hu, Guofeng Wu

“…To obtain a well-trained 2D ConvNet, a training sample expansion strategy was introduced to assist context feature learning. The H-ConvNet was tested in six highly heterogeneous urban regions around the world, and it was compared with support vector machine (SVM), object-based image analysis (OBIA), Markov random field model (MRF) and a newly proposed patch-based ConvNet system. …”
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A Multiscale Cross Interaction Attention Network for Hyperspectral Image Classification by Dongxu Liu, Yirui Wang, Peixun Liu, Qingqing Li, Hang Yang, Dianbing Chen, Zhichao Liu, Guangliang Han

“…However, two challenging problems still exist: the first challenge is that redundant information is averse to feature learning, which damages the classification performance; the second challenge is that most of the existing classification methods only focus on single-scale feature extraction, resulting in underutilization of information. …”
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A Hybrid-Scale Feature Enhancement Network for Hyperspectral Image Classification by Dongxu Liu, Tao Shao, Guanglin Qi, Meihui Li, Jianlin Zhang

“…Additionally, previous works usually focused on utilizing fixed-scale convolutional kernels or multiple available, receptive fields with varying scales to capture features, which leads to the underutilization of information and is vulnerable to feature learning. To remedy the above issues, we propose an innovative hybrid-scale feature enhancement network (HFENet) for HSI classification. …”
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A joint algorithm of multi-target detection and tracking for underground miners by ZHOU Mengran, LI Xuesong, ZHU Ziwei, HUANG Kaiwen

“…In the part of multi-target tracking, the omni-scale network (OSNet) is used to replace the shallow residual network in Deep SORT to carry out omni-directional feature learning. Therefore, pedestrian re-identification and target tracking precision are improved. …”
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DPSSD: Dual-Path Single-Shot Detector by Dongri Shan, Yalu Xu, Peng Zhang, Xiaofang Wang, Dongmei He, Chenglong Zhang, Maohui Zhou, Guoqi Yu

“…Our improved dual-path network is more adaptable to multi-scale object detection tasks, and we combine it with the feature fusion module to generate a multi-scale feature learning paradigm called the “Dual-Path Feature Pyramid”. …”
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Deep-Deterministic Policy Gradient Based Multi-Resource Allocation in Edge-Cloud System: A Distributed Approach by Arslan Qadeer, Myung Jong Lee

“…To this end, we propose a deep-deterministic policy gradient (DDPG) based temporal feature learning attentional network (TFLAN) model to address the MRA problem. …”
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A Deep-Learning-Based Multimodal Data Fusion Framework for Urban Region Function Recognition by Mingyang Yu, Haiqing Xu, Fangliang Zhou, Shuai Xu, Hongling Yin

“…In the first part of the complementary feature-learning and fusion module, we use a convolutional neural network (CNN) to extract visual features and social features. …”
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Learning from few subjects with large amounts of voice monitoring data by Gonzalez Ortiz, Jose Javier.

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