A Sound and Vibration Fusion Method for Fault Diagnosis of Rolling Bearings under Speed-Varying Conditions by Haibo Wan, Xiwen Gu, Shixi Yang, Yanni Fu

“…The diagnosis accuracy improves by sound and vibration fusion and multiscale feature learning.…”
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GSB: GNGS and SAG-BiGRU network for malware dynamic detection. by Zhanhui Hu, Guangzhong Liu, Xinyu Xiang, Yanping Li, Siqing Zhuang

“…For solving the problem, this study proposed the GNGS algorithm to construct a new balance dataset for the model algorithm to pay more attention to the feature learning of the minority attacks' malware to improve the detection rate of attacks' malware. …”
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Deep transfer learning for aortic root dilation identification in 3D ultrasound images by Hagenah Jannis, Heinrich Mattias, Ernst Floris

“…In previously published approaches, handcrafted features showed a limited classification accuracy. However, feature learning is insufficient due to the small data sets available for this specific problem. …”
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U-SSD: Improved SSD Based on U-Net Architecture for End-to-End Table Detection in Document Images by Shih-Hsiung Lee, Hung-Chun Chen

“…Therefore, this paper proposes an end-to-end table detection model, U-SSD, as based on the object detection method of deep learning, takes the Single Shot MultiBox Detector (SSD) as the basic model architecture, improves it by U-Net, and adds dilated convolution to enhance the feature learning capability of the network. The experiment in this study uses the dataset of accident claim documents, as provided by a Taiwanese Law Firm, and conducts table detection. …”
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V²ReID: Vision-Outlooker-Based Vehicle Re-Identification by Yan Qian, Johan Barthelemy, Umair Iqbal, Pascal Perez

“…Furthermore, due to small inter-class similarities and large intra-class differences, feature learning is often enhanced with non-visual cues, such as the topology of camera networks and temporal information. …”
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Unsupervised Deep Noise Modeling for Hyperspectral Image Change Detection by Xuelong Li, Zhenghang Yuan, Qi Wang

“…Though deep convolutional neural networks (CNN) have superior capability in high-level semantic feature learning, it is difficult to employ CNN for change detection tasks. …”
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Point cloud completion via structured feature maps using a feedback network by Zejia Su, Haibin Huang, Chongyang Ma, Hui Huang, Ruizhen Hu

“…Abstract In this paper, we tackle the challenging problem of point cloud completion from the perspective of feature learning. Our key observation is that to recover the underlying structures as well as surface details, given partial input, a fundamental component is a good feature representation that can capture both global structure and local geometric details. …”
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Progressively Hybrid Transformer for Multi-Modal Vehicle Re-Identification by Wenjie Pan, Linhan Huang, Jianbao Liang, Lan Hong, Jianqing Zhu

“…The local region hybrider fuses the cropped regions to let regions of each modal bring local structural characteristics of all modalities, mitigating modal differences at the beginning of feature learning. Regarding the FHM, a modal-specific controller and a modal information embedding are designed to effectively fuse multi-modal information at the feature level. …”
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DINA: Deformable INteraction Analogy by Zeyu Huang, Sisi Dai, Kai Xu, Hao Zhang, Hui Huang, Ruizhen Hu

“…Qualitative and quantitative experiments show that our ITF-guided deformable interaction analogy works surprisingly well even with simple distance features compared to variants of state-of-the-art methods that utilize more sophisticated interaction representations and feature learning from large datasets.…”
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A novel parameter decoupling approach of personalised federated learning for image analysis by Ruizheng Su, Xiongwen Pang, Hui Wang

“…It should be emphasised that the authors’ personalised federated learning method decouples the personalised (P) layers into a connecting (C) layer and classifier (C) layer in order to enhance the effectiveness of feature learning for personalised layers. Further, an approach is proposed to fully use the base layers to adapt a personalised model based on the newly admitted institution's dataset through meta‐transfer. …”
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KappaFace: Adaptive Additive Angular Margin Loss for Deep Face Recognition by Chingis Oinar, Binh M. Le, Simon S. Woo

“…Feature learning is a widely used method for large-scale face recognition tasks. …”
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Multi-Receptive Field Soft Attention Part Learning for Vehicle Re-Identification by Xiyu Pang, Yilong Yin, Yanli Zheng

“…Extensive ablation experiments demonstrate the effectiveness of our part-level feature learning method MRF-SAPL, and our model achieves state-of-the-art performance on two benchmark datasets.…”
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Scale robust point matching‐Net: End‐to‐end scale point matching using Lie group by Xin Wang, Hui Ding, Guangwei Zhao, Yaxin Peng, Chaomin Shen

“…Secondly, it introduces a new feature learning module, which better preserves the shape structure by aggregating the high‐dimensional feature and calculating the normal vector of point cloud surface automatically. …”
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MFCNet: Mining Features Context Network for RGB–IR Person Re-Identification by Jing Mei, Huahu Xu, Yang Li, Minjie Bian, Yuzhe Huang

“…However, most global shared feature learning methods are sensitive to background clutter, and contextual feature relationships are not considered among the mined features. …”
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Synergistic 2D/3D Convolutional Neural Network for Hyperspectral Image Classification by Xiaofei Yang, Xiaofeng Zhang, Yunming Ye, Raymond Y. K. Lau, Shijian Lu, Xutao Li, Xiaohui Huang

“…The SyCNN consists of a hybrid module that combines 2D and 3D CNNs in feature learning and a data interaction module that fuses spectral and spatial hyperspectral information. …”
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Deep Supervised Residual Dense Network for Underwater Image Enhancement by Yanling Han, Lihua Huang, Zhonghua Hong, Shouqi Cao, Yun Zhang, Jing Wang

“…In recent years, deep learning has been widely used in underwater image enhancement and restoration because of its powerful feature learning capabilities, but there are still shortcomings in detailed enhancement. …”
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Fault Diagnosis of Rotating Machinery Using Denoising-Integrated Sparse Autoencoder Based Health State Classification by Jing Yang, Guo Xie, Yanxi Yang

“…The innovation points of this study mainly include: 1) A feature-enhanced and denoising solution based on fault sensitivity degree (FSD) is designed, and the reconstructed diagnostic signals are acquired. 2) A disassociation framework is formulated, and the data coupling is solved. 3) A weight constraint term of SAE is constructed to improve the effectiveness and diversity of feature learning. 4) An adaptive loss function and a DISAE model is formed, and the early compound faults diagnosis is achieved. …”
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Driving posture recognition by convolutional neural networks by Chao Yan, Frans Coenen, Bailing Zhang

“…In the authors' works, a CNN model was first pre‐trained by an unsupervised feature learning method called sparse filtering, and subsequently fine‐tuned with classification. …”
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A New Dual-Input Deep Anomaly Detection Method for Early Faults Warning of Rolling Bearings by Yuxiang Kang, Guo Chen, Hao Wang, Wenping Pan, Xunkai Wei

“…At the same time, the experience pool structure is introduced to improve the feature learning ability of the network. A new objective loss function is also proposed to learn the network parameters. …”
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Acute coronary syndrome risk prediction based on gradient boosted tree feature selection and recursive feature elimination: A dataset-specific modeling study. by Huizhong Lin, Yutao Xue, Kaizhi Chen, Shangping Zhong, Lianglong Chen

“…In response to this problem, this paper uses a hybrid feature selection method based on gradient boosting trees and recursive feature elimination with cross-validation (RFECV) to reduce ACS feature redundancy and uses interpretable feature learning for feature selection to retain the most discriminative features. …”
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