Noise Eliminated Ensemble Empirical Mode Decomposition Scalogram Analysis for Rotating Machinery Fault Diagnosis by Atik Faysal, Wai Keng Ngui, Meng Hee Lim, Mohd Salman Leong

“…A convolution neural network (CNN) classifier was applied for classification because of its feature learning ability. A generalized CNN architecture was proposed to reduce the model training time. …”
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Semantic segmentation for plastic-covered greenhouses and plastic-mulched farmlands from VHR imagery by Bowen Niu, Quanlong Feng, Shuai Su, Zhi Yang, Sihang Zhang, Shaotong Liu, Jiudong Wang, Jianyu Yang, Jianhua Gong

“…Specifically, the proposed semantic segmentation model has an encoder-decoder structure, where the encoder is composed of a new convolutional neural network for discriminative spatial feature learning, while the decoder utilizes a multi-task strategy to improve the predictions on the boundaries. …”
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Pattern Recognition of Different Window Size Control Charts Based on Convolutional Neural Network and Information Fusion by Tao Zan, Zifeng Su, Zhihao Liu, Deyin Chen, Min Wang, Xiangsheng Gao

“…After undergoing feature learning, CNN is used to extract the best feature set from the control chart, while at the same time, expert features (including one shape features and four statistical features) are fused to complete the CCPR. …”
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Improved chimp optimization algorithm (ICOA) feature selection and deep neural network framework for internet of things (IOT) based android malware detection by Tirumala Vasu G, Samreen Fiza, ATA. Kishore Kumar, V. Sowmya Devi, Ch Niranjan Kumar, Afreen Kubra

“…Furthermore, these techniques face two major obstacles: How to acquire useful feature representations from raw data; How to lessen feature learning's reliance on past knowledge or individual workers. …”
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A Framework for Robust Deep Learning Models Against Adversarial Attacks Based on a Protection Layer Approach by Mohammed Nasser Al-Andoli, Shing Chiang Tan, Kok Swee Sim, Pey Yun Goh, Chee Peng Lim

“…The framework leverages convolutional neural networks (CNNs) for feature learning, Deep Neural Networks (DNNs) with softmax for classification, and a defense mechanism to identify and exclude AEs. …”
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Developing a Dual-Stream Deep-Learning Neural Network Model for Improving County-Level Winter Wheat Yield Estimates in China by Hai Huang, Jianxi Huang, Quanlong Feng, Junming Liu, Xuecao Li, Xinlei Wang, Quandi Niu

“…The model consists of two branches for robust feature learning: one for sequential data (remote sensing and weather series data) and the other for statical data (soil properties). …”
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KDA3D: Key-Point Densification and Multi-Attention Guidance for 3D Object Detection by Jiarong Wang, Ming Zhu, Bo Wang, Deyao Sun, Hua Wei, Changji Liu, Haitao Nie

“…Our designed lightweight point-wise and channel-wise attention modules can adaptively strengthen the “skeleton” and “distinctiveness” point-features to help feature learning networks capture more representative or finer patterns. …”
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Reliable and stable fundus image registration based on brain-inspired spatially-varying adaptive pyramid context aggregation network by Jie Xu, Kang Yang, Youxin Chen, Liming Dai, Dongdong Zhang, Ping Shuai, Ping Shuai, Rongjie Shi, Zhanbo Yang

“…Due to the strong feature learning ability of deep neural network, current image registration methods based on deep learning directly learn to align the geometric transformation between the reference image and test image in an end-to-end manner. …”
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Hybrid SVM-CNN Classification Technique for Human–Vehicle Targets in an Automotive LFMCW Radar by Qisong Wu, Teng Gao, Zhichao Lai, Dianze Li

“…The proposed SVM-CNN approach takes full advantage of both the locations of underlying class in the entire Range-Doppler image and automatical local feature learning in the CNN with sliding filter bank to improve the classification performance. …”
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Perceptual Metric Guided Deep Attention Network for Single Image Super-Resolution by Yubao Sun, Yuyang Shi, Ying Yang, Wangping Zhou

“…Deep learning has been widely applied to image super-resolution (SR) tasks and has achieved superior performance over traditional methods due to its excellent feature learning capabilities. However, most of these deep learning-based methods require training image sets to pre-train SR network parameters. …”
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Enhancing Fingerprint Liveness Detection Accuracy Using Deep Learning: A Comprehensive Study and Novel Approach by Deep Kothadiya, Chintan Bhatt, Dhruvil Soni, Kalpita Gadhe, Samir Patel, Alessandro Bruno, Pier Luigi Mazzeo

“…Spatial attention (SA) and channel attention (CA) models were used sequentially to enhance feature learning. A three-fold sequential attention model is used along with five convolution learning layers. …”
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Local and Global Spectral Features for Hyperspectral Image Classification by Zeyu Xu, Cheng Su, Shirou Wang, Xiaocan Zhang

“…Recently, data-driven methods, especially the use of convolutional neural networks (CNNs), have shown great improvements in performance when processing image data owing to their powerful automatic feature learning and extraction abilities and are also widely used for HSI feature extraction and classification. …”
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Fully Unsupervised Person Re-Identification via Multiple Pseudo Labels Joint Training by Qing Tang, Ge Cao, Kang-Hyun Jo

“…Unlike human-annotated true labels, the pseudo labels contain noise labels which substantially hinder the network’s capability on feature learning. In order to refine the predicted pseudo labels, we introduce a novel unsupervised re-ID method named Multiple pseudo Labels Joint Training (MLJT) in this paper. …”
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Multi-View Information Fusion Fault Diagnosis Method Based on Attention Mechanism and Convolutional Neural Network by Hongmei Li, Jinying Huang, Minjuan Gao, Luxia Yang, Yichen Bao

“…A multi-channel fusion convolutional neural network was used for feature learning. In addition, the channel attention mechanism was used to learn the view weight, so that the algorithm could pay more attention to the views that contribute more to the fault identification task during the training process, and more reasonably integrate the information of different views. …”
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Improvements Based on ShuffleNetV2 Model for Bird Identification by Liu-Lei Zhang, Ying Jiang, You-Peng Sun, Yuan Zhang, Zheng Wang

“…In ShuffleNetV2 network, there is no feature fusion module and efficient attention mechanism to assist the feature learning of the model. Therefore, this paper adds a feature fusion module and two attention mechanisms to make up for this shortcoming. …”
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IRSTFormer: A Hierarchical Vision Transformer for Infrared Small Target Detection by Gao Chen, Weihua Wang, Sirui Tan

“…However, the traditional model-driven methods do not have the capability of feature learning, resulting in poor adaptability to various scenes. …”
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Locality Preserving Property Constrained Contrastive Learning for Object Classification in SAR Imagery by Jing Wang, Sirui Tian, Xiaolin Feng, Bo Zhang, Fan Wu, Hong Zhang, Chao Wang

“…Robust unsupervised feature learning is a critical yet tough task for synthetic aperture radar (SAR) automatic target recognition (ATR) with limited labeled data. …”
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Deep-Learning Multiscale Digital Holographic Intensity and Phase Reconstruction by Bo Chen, Zhaoyi Li, Yilin Zhou, Yirui Zhang, Jingjing Jia, Ying Wang

“…For holograms with uneven distribution of useful information, local feature extraction is performed to generate holograms of different scales, branch input training is used to realize multiscale feature learning, and feature information of different receptive fields is obtained. …”
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DS-YOLOv8-Based Object Detection Method for Remote Sensing Images by Lingyun Shen, Baihe Lang, Zhengxun Song

“…It aims to overcome limitations such as the restricted receptive field caused by fixed convolutional kernels in the YOLO backbone network and the inadequate multi-scale feature learning capabilities resulting from the spatial and channel attention fusion mechanism’s inability to adapt to the input data’s feature distribution. …”
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Language-independent methods for computer-assisted pronunciation training by Lee, Ann, Ph. D. Massachusetts Institute of Technology

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