Self-Supervised Representation Learning for Remote Sensing Image Change Detection Based on Temporal Prediction by Huihui Dong, Wenping Ma, Yue Wu, Jun Zhang, Licheng Jiao

“…Recently, deep learning techniques have reported compelling performance on robust feature learning. However, generating accurate semantic supervision that reveals real change information in satellite images still remains challenging, especially for manual annotation. …”
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ECNN: Intelligent Fault Diagnosis Method Using Efficient Convolutional Neural Network by Chao Zhang, Qixuan Huang, Chaoyi Zhang, Ke Yang, Liye Cheng, Zhan Li

“…With outstanding deep feature learning and nonlinear classification abilities, Convolutional Neural Networks (CNN) have been gradually applied to deal with various fault diagnosis tasks. …”
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A Change Detection Method Based on Multi-Scale Adaptive Convolution Kernel Network and Multimodal Conditional Random Field for Multi-Temporal Multispectral Images by Shou Feng, Yuanze Fan, Yingjie Tang, Hao Cheng, Chunhui Zhao, Yaoxuan Zhu, Chunhua Cheng

“…Multispectral images usually contain many complex scenes, such as ground objects with diverse scales and proportions, so the change detection task expects the feature extractor is superior in adaptive multi-scale feature learning. To address the above-mentioned problems, a multispectral image change detection method based on multi-scale adaptive kernel network and multimodal conditional random field (MSAK-Net-MCRF) is proposed. …”
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Divide-and-Attention Network for HE-Stained Pathological Image Classification by Rui Yan, Zhidong Yang, Jintao Li, Chunhou Zheng, Fa Zhang

“…With such decomposed pathological images, the DANet first performs feature learning independently in each branch, and then focuses on the most important feature representation through the branch selection attention module. …”
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A New Road Damage Detection Baseline with Attention Learning by Hongwei Zhang, Zhaohui Wu, Yuxuan Qiu, Xiangcheng Zhai, Zichen Wang, Peng Xu, Zhenzheng Liu, Xiantong Li, Na Jiang

“…Although some public datasets provide a database for the development of ADRD, their amounts of data and the standard of classification cannot meet network training and feature learning. With the aim of solving this problem, this work publishes a new road damage dataset named CNRDD, which is labeled according to the latest evaluation standard for highway technical conditions in China (JTG5210-2018). …”
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CM-NET: Cross-Modal Learning Network for CSI-Based Indoor People Counting in Internet of Things by Jing Guo, Xiaokang Gu, Zhengqi Liu, Minghao Ji, Jingwen Wang, Xiaoyan Yin, Pengfei Xu

“…Owing to the complexity of human location-random environments, the transformer model cannot extract characteristics describing the number of people. To enhance the feature learning capability of the transformer model, CM-NET takes the feature knowledge learned by the image-based people counting model to supervise the learning process. …”
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Single-Trial Recognition of Video Gamer’s Expertise from Brain Haemodynamic and Facial Emotion Responses by Ana R. Andreu-Perez, Mehrin Kiani, Javier Andreu-Perez, Pratusha Reddy, Jaime Andreu-Abela, Maria Pinto, Kurtulus Izzetoglu

“…From the data collected, i.e., gamer’s fNIRS data in combination with emotional state estimation from gamer’s facial expressions, the expertise level of the gamers has been decoded per trial in a multi-modal framework comprising of unsupervised deep feature learning and classification by state-of-the-art models. …”
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Attribute-Guided Multi-Level Attention Network for Fine-Grained Fashion Retrieval by Ling Xiao, Toshihiko Yamasaki

“…This can further alleviate the feature gap problem by perturbing object-centric feature learning. Moreover, we propose an improved attribute-guided attention module for extracting more accurate attribute-specific representations. …”
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Spatial-temporal transformer network for multi-year ENSO prediction by Dan Song, Xinqi Su, Wenhui Li, Zhengya Sun, Tongwei Ren, Wen Liu, An-An Liu

“…To solve these problem, we propose a spatio-temporal transformer network to model the inherent characteristics of the sea surface temperature anomaly map and heat content anomaly map along with the changes in space and time by designing an effective attention mechanism, and innovatively incorporate temporal index into the feature learning procedure to model the influence of seasonal variation on the prediction of the ENSO phenomenon. …”
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Composition of Hybrid Deep Learning Model and Feature Optimization for Intrusion Detection System by Azriel Henry, Sunil Gautam, Samrat Khanna, Khaled Rabie, Thokozani Shongwe, Pronaya Bhattacharya, Bhisham Sharma, Subrata Chowdhury

“…Current approaches are aligned towards deep learning (DL) methods for IDSs, but the success of the DL mechanism depends on the feature learning process, which is an open challenge. Thus, in this paper, the authors propose a technique which combines both CNN, and GRU, where different CNN–GRU combination sequences are presented to optimize the network parameters. …”
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Multimodal EEG Emotion Recognition Based on the Attention Recurrent Graph Convolutional Network by Jingxia Chen, Yang Liu, Wen Xue, Kailei Hu, Wentao Lin

“…This proposed method performs feature learning in three dimensions of time, space, and frequency by excavating the complementary relationship of different modal data so that the learned deep emotion-related features are more discriminative. …”
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Semi-Supervised Seizure Prediction Model Combining Generative Adversarial Networks and Long Short-Term Memory Networks by Xiaoli Yang, Lipei Liu, Zhenwei Li, Yuxin Xia, Zhipeng Fan, Jiayi Zhou

“…Specifically, we utilize the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) as the feature learning model, using the Earth Mover’s distance and gradient penalty to guide the unsupervised training process and train a high-order feature extractor. …”
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Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach by Zhi-Xin Yang, Xian-Bo Wang, Jian-Hua Zhong

“…The framework enables both representational feature learning and fault classification. The multi-layered ELM based representational learning covers functions including data preprocessing, feature extraction and dimension reduction. …”
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Hybrid Dense Network With Attention Mechanism for Hyperspectral Image Classification by Muhammad Ahmad, Adil Mehmood Khan, Manuel Mazzara, Salvatore Distefano, Swalpa Kumar Roy, Xin Wu

“…However, fixed kernel sizes make traditional CNN too specific, neither flexible nor conducive to feature learning, thus impacting on the classification accuracy. …”
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Ensemble streamflow forecasting based on variational mode decomposition and long short term memory by Xiaomei Sun, Haiou Zhang, Jian Wang, Chendi Shi, Dongwen Hua, Juan Li

“…VMD-LSTM-GBRT was compared with respect to three aspects: (1) feature extraction algorithm; ensemble empirical mode decomposition (EEMD) was used. (2) Feature learning techniques; deep neural networks (DNNs) and support vector machines for regression (SVRs) were exploited. (3) Ensemble strategy; the summation strategy was used. …”
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Ship Infrared Automatic Target Recognition Based on Bipartite Graph Recommendation: A Model-Matching Method by Haoxiang Zhang, Chao Liu, Jianguang Ma, Hui Sun

“…This method establishes evaluation criteria based on recognition accuracy and feature learning credibility, uncovering the underlying connections between IR attributes of ships and candidate models. …”
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Deforestation detection using deep learning-based semantic segmentation techniques: a systematic review by Imran Md Jelas, Imran Md Jelas, Mohd Asyraf Zulkifley, Mardina Abdullah, Martin Spraggon

“…The review underscores the pivotal role of satellite imagery in capturing spatial information and highlights the strengths of various deep learning architectures in deforestation analysis. Multiscale feature learning and fusion emerge as critical strategies enabling deep networks to comprehend contextual nuances across various scales. …”
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GPR Data Reconstruction Using Residual Feature Distillation Block U-Net by Qianwei Dai, Yue He, Yi Lei, Jianwei Lei, Xiangyu Wang, Bin Zhang

“…To be specific, by employing the information distillation network based on the multiple feature extraction connections, RFDB is capable of utilizing the adequate residual information of each layer for feature learning. Moreover, a skip connection is additional patched on the residual units to properly compensate the missing features in the convolution process. …”
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End-to-End Visual Domain Adaptation Network for Cross-Domain 3D CPS Data Retrieval by An-An Liu, Shu Xiang, Wei-Zhi Nie, Dan Song

“…Specifically, an center-based discriminative feature learning method enables the domain invariant features with better intra-class compactness and inter-class separability. …”
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COVID-19 Infection Segmentation and Severity Assessment Using a Self-Supervised Learning Approach by Yao Song, Jun Liu, Xinghua Liu, Jinshan Tang

“…For COVID-19 severity classification, the proposed method outperformed other unsupervised feature learning methods by about 7.16% in accuracy. For segmentation, when the amount of labeled data was 100%, the Dice value of the proposed method was 5.58% higher than that of U-Net.; in 70% of the cases, our method was 8.02% higher than U-Net; in 30% of the cases, our method was 11.88% higher than U-Net; and in 10% of the cases, our method was 16.88% higher than U-Net. …”
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