Self-Supervised Audio-Visual Feature Learning for Single-Modal Incremental Terrain Type Clustering by Reina Ishikawa, Ryo Hachiuma, Hideo Saito

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Graph Spectral Domain Feature Learning With Application to in-Air Hand-Drawn Number and Shape Recognition by Basheer Alwaely, Charith Abhayaratne

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Anti-Software Attack Ear Identification System Using Deep Feature Learning and Blockchain Protection by Xuebin Xu, Yibiao Liu, Chenguang Liu, Longbin Lu

“…This paper proposes a software-attack-proof ear recognition system using deep feature learning and blockchain protection to address the problem that the recognition performance of existing systems is generally poor in the face of unconstrained ear databases in realistic scenarios. …”
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Medical Data Feature Learning Based on Probability and Depth Learning Mining: Model Development and Validation by Yang, Yuanlin, Li, Dehua

“…To develop a medical data feature learning model based on probabilistic and deep learning mining, we mined information from medical big data and developed an intelligent application that studies the differences in medical data for disease risk assessment and enables feature learning of the related multimodal data. …”
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Deep attentive spatio-temporal feature learning for automatic resting-state fMRI denoising by Keun-Soo Heo, Dong-Hee Shin, Sheng-Che Hung, Weili Lin, Han Zhang, Dinggang Shen, Tae-Eui Kam

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MLGN: multi-scale local-global feature learning network for long-term series forecasting by Maowei Jiang, Kai Wang, Yue Sun, Wenbo Chen, Bingjie Xia, Ruiqi Li

“…Furthermore, our proposed method,multi-scale local-global feature learning network (MLGN), achieves a time and memory complexity of O ( L ) and consistently achieve state-of-the-art results on six benchmark datasets. …”
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MLFLHMDA: predicting human microbe-disease association based on multi-view latent feature learning by Ziwei Chen, Liangzhe Zhang, Jingyi Li, Mingyang Fu

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Human Action Recognition of Spatiotemporal Parameters for Skeleton Sequences Using MTLN Feature Learning Framework by Faisal Mehmood, Enqing Chen, Muhammad Azeem Akbar, Abeer Abdulaziz Alsanad

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Enhanced Feature Extraction Network Based on Acoustic Signal Feature Learning for Bearing Fault Diagnosis by Yuanqing Luo, Wenxia Lu, Shuang Kang, Xueyong Tian, Xiaoqi Kang, Feng Sun

“…This paper proposes an enhanced feature extraction network (EFEN) for fault diagnosis of rolling bearings based on acoustic signal feature learning. The EFEN network comprises four main components: the data preprocessing module, the information feature selection module (IFSM), the channel attention mechanism module (CAMM), and the convolutional neural network module (CNNM). …”
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Global Co-Occurrence Feature and Local Spatial Feature Learning for Skeleton-Based Action Recognition by Jun Xie, Wentian Xin, Ruyi Liu, Qiguang Miao, Lijie Sheng, Liang Zhang, Xuesong Gao

“…Accordingly, to address these issues, we propose a Global Co-occurrence feature and Local Spatial feature learning model (GCLS) consisting of two branches. …”
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Remote sensing scene classification based on rotation-invariant feature learning and joint decision making by Yong Zhou, Xuning Liu, Jiaqi Zhao, Ding Ma, Rui Yao, Bing Liu, Yi Zheng

“…To overcome this problem, we propose a rotation-invariant feature learning and joint decision-making method based on Siamese convolutional neural networks with the combination of identification and verification models. …”
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Box2Seg: Attention weighted loss and discriminative feature learning for weakly supervised segmentation by Kulharia, V, Chandra, S, Agrawal, A, Torr, PHS, Tyagi, A

Infproto-Powered Adaptive Classifier and Agnostic Feature Learning for Single Domain Generalization in Medical Images by Guo, X, Liu, J, Yuan, Y

Calcification detection of coronary artery disease in intravascular ultrasound image: Deep feature learning approach by Sofian, Hannah, Chia, Joel Ming Than, Mohammad, Suraya, Mohd. Noor, Norliza

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View Enhanced Jigsaw Puzzle for Self-Supervised Feature Learning in 3D Human Action Recognition by Wei You, Xue Wang

“…In addition, by adjusting the difficulty of VEJP, the influence of the pretext task difficulty on the downstream task performance is studied, and the experimental results show that the pretext tasks should be moderately difficult to achieve effective feature learning. Our method achieves competitive results on representative benchmark datasets. …”
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Efficient Feature Learning Approach for Raw Industrial Vibration Data Using Two-Stage Learning Framework by Mohamed-Ali Tnani, Paul Subarnaduti, Klaus Diepold

Subjects: “…feature learning…”
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SANet: A Sea–Land Segmentation Network Via Adaptive Multiscale Feature Learning by Binge Cui, Wei Jing, Ling Huang, Zhongrui Li, Yan Lu

“…First, to integrate the spectral, textural, and semantic features of ground objects at different scales, we designed an adaptive multiscale feature learning module (AML) to replace the conventional serial convolution operation. …”
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Survey on Encoding Schemes for Genomic Data Representation and Feature Learning—From Signal Processing to Machine Learning by Ning Yu, Zhihua Li, Zeng Yu

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Mixup Feature: A Pretext Task Self-Supervised Learning Method for Enhanced Visual Feature Learning by Jiashu Xu, Sergii Stirenko

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SparNet: A Convolutional Neural Network for EEG Space-Frequency Feature Learning and Depression Discrimination by Xin Deng, Xufeng Fan, Xiangwei Lv, Kaiwei Sun

“…However, there are few studies on designing convolution filters for spatial and frequency domain feature learning in different brain regions. In this study, SparNet, a convolutional neural network composed of five parallel convolutional filters and the SENet, is proposed to learn EEG space-frequency domain characteristics and distinguish between depressive and normal control. …”
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