GMFAD: Towards generalized visual recognition via multilayer feature alignment and disentanglement by Li, Haoliang, Wang, Shiqi, Wan, Renjie, Kot, Alex Chichung

“…Inspired by the hierarchical organization of deep feature representation that progressively leads to more abstract features at higher layers of representations, we propose to tackle this problem with a novel feature learning framework, which is called GMFAD, with better generalization capability in a multilayer perceptron manner. …”
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Learning the structure of object categories from incomplete supervision by Novotny, D

“…</p> <p>A similar feature learning machine leveraging the equivariance constraint is later introduced. …”

Improved deep learning techniques for recognition and labeling by Abrar Hamzeh Saad Abdul Nabi

“…Researchers tend to rely on hand-crafted features like SIFT and HOG which lack the ability to be data-adaptive, hence, feature learning becomes more favorable in the last few years. …”
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Noise eliminated ensemble empirical mode decomposition scalogram analysis for rotating machinery fault diagnosis by Atik, Faysal

“…A convolution neural network (CNN) classifier was applied for classification because of its feature-learning ability. A generalised CNN architecture was proposed to reduce the model training time. …”
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Phenotyping agronomic and physiological traits in peanut under mid‐season drought stress using UAV‐based hyperspectral imaging and machine learning by Kamand Bagherian, Rafael Bidese‐Puhl, Yin Bao, Qiong Zhang, Alvaro Sanz‐Saez, Phat M. Dang, Marshall C. Lamb, Charles Chen

“…Predictions of the agronomic traits obtained using feature learning and DL (R2 = 0.45–0.73; symmetric mean absolute percentage error [sMAPE] = 24%–51%) outperformed those obtained using feature engineering and conventional ML models (R2 = 0.44–0.61, sMAPE = 27%–59%). …”
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Spatio-Temporal Features Representation Using Recurrent Capsules for Monaural Speech Enhancement by Jawad Ali, Nasir Saleem, Sami Bourouis, Eatedal Alabdulkreem, Hela El Mannai, Sami Dhahbi

“…A convolutional neural network (CNN) successfully learns feature representations from speech spectrograms but loses spatial information due to distortion, which is important for humans to understand speech. Speech feature learning is an important ongoing research to capture higher-level representations of speech that go beyond conventional techniques. …”
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A twin convolutional neural network with hybrid binary optimizer for multimodal breast cancer digital image classification by Olaide N. Oyelade, Eric Aghiomesi Irunokhai, Hui Wang

“…First, modality-based feature learning was achieved by extracting both low and high levels features using the networks embedded with TwinCNN. …”
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TCFLTformer: TextCNN-Flat-Lattice Transformer for Entity Recognition of Air Traffic Management Cyber Threat Knowledge Graphs by Chao Liu, Buhong Wang, Zhen Wang, Jiwei Tian, Peng Luo, Yong Yang

“…To solve these problems, a TextCNN-Flat-Lattice Transformer (TCFLTformer) with CNN-Transformer hybrid architecture is proposed for ATM cyber threat entity recognition, in which a relative positional embedding (RPE) is designed to encode position text content information, and a multibranch prediction head (MBPH) is utilized to enhance deep feature learning. TCFLTformer first uses CNN to carry out convolution and pooling operations on the text to extract local features and then uses a Flat-Lattice Transformer to learn temporal and relative positional characteristics of the text to obtain the final annotation results. …”
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TasselLFANet: a novel lightweight multi-branch feature aggregation neural network for high-throughput image-based maize tassels detection and counting by Zhenghong Yu, Jianxiong Ye, Cuina Li, Huabing Zhou, Xun Li

“…Our proposed approach improves the feature-learning ability of TasselLFANet by adopting a cross-stage fusion strategy that balances the variability of different layers. …”
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Early Detection of Wheat Yellow Rust Disease and Its Impact on Terminal Yield with Multi-Spectral UAV-Imagery by Canh Nguyen, Vasit Sagan, Juan Skobalski, Juan Ignacio Severo

“…A custom 3-dimensional convolutional neural network (3D-CNN) relying on the feature learning mechanism was an alternative prediction method. …”
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A coarse-to-fine point completion network with details compensation and structure enhancement by Yongwei Miao, Chengyu Jing, Weihao Gao, Xudong Zhang

“…The coarse completion stage of our network consists of two branches—a shape structure recovery branch and a local details compensation branch, which can recover the overall shape of the underlying model and the shape details of incomplete point cloud through feature learning and hierarchical feature fusion. The fine completion stage of our network employs the structure enhancement module to reinforce the correlated shape structures of the coarse repaired shape (such as regular arrangement or symmetry), thus obtaining the completed geometric shape with finer-grained details. …”
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Learning Multi-Types of Neighbor Node Attributes and Semantics by Heterogeneous Graph Transformer and Multi-View Attention for Drug-Related Side-Effect Prediction by Ping Xuan, Peiru Li, Hui Cui, Meng Wang, Toshiya Nakaguchi, Tiangang Zhang

“…Finally, a pairwise multi-view feature learning module based on capsule networks was built to learn the pairwise attributes from the heterogeneous graphs. …”
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Recognizing Semi-Natural and Spontaneous Speech Emotions Using Deep Neural Networks by Ammar Amjad, Lal Khan, Noman Ashraf, Muhammad Bilal Mahmood, Hsien-Tsung Chang

“…The architecture of both models consists of five local feature learning blocks (LFLBs), two LSTM layers, and a fully connected layer (FCL). …”
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Explainable Artificial Intelligence (XAI) in Pain Research: Understanding the Role of Electrodermal Activity for Automated Pain Recognition by Philip Gouverneur, Frédéric Li, Kimiaki Shirahama, Luisa Luebke, Wacław M. Adamczyk, Tibor M. Szikszay, Kerstin Luedtke, Marcin Grzegorzek

“…Thus, the use of complex neural networks should be questioned in pain recognition, especially considering their computational costs; and (2) both traditional feature engineering and deep feature learning approaches rely on simple characteristics of the input time-series data to make their decision in the context of automated pain recognition.…”
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An improved fused feature residual network for 3D point cloud data by Abubakar Sulaiman Gezawa, Chibiao Liu, Heming Jia, Y. A. Nanehkaran, Mubarak S. Almutairi, Haruna Chiroma

“…This study proposes an improved fused feature network as well as a comprehensive framework for solving shape classification and segmentation tasks using a two-branch technique and feature learning. We begin by designing a feature encoding network with two distinct building blocks: layer skips within, batch normalization (BN), and rectified linear units (ReLU) in between. …”
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A New Dataset and Deep Residual Spectral Spatial Network for Hyperspectral Image Classification by Yiming Xue, Dan Zeng, Fansheng Chen, Yueming Wang, Zhijiang Zhang

“…Otherwise, to reduce overfitting caused by the imbalance between high dimension and small quantity of labeled HSI data, existing CNNs for HSI classification are relatively shallow and suffer from low capacity of feature learning. To solve this problem, we proposed an HSI classification framework named deep residual spectral spatial setwork (DRSSN). …”
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Self-Supervised Feature Representation for SAR Image Target Classification Using Contrastive Learning by Hao Pei, Mingjie Su, Gang Xu, Mengdao Xing, Wei Hong

“…Numerical experiments are carried out on the shared MSTAR dataset to demonstrate that the model based on the proposed self-supervised feature learning algorithm is superior to the conventional supervised methods under labeled data constraints. …”
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Multiscale Pixel-Level and Superpixel-Level Method for Hyperspectral Image Classification: Adaptive Attention and Parallel Multi-Hop Graph Convolution by Junru Yin, Xuan Liu, Ruixia Hou, Qiqiang Chen, Wei Huang, Aiguang Li, Peng Wang

“…Extensive evaluation experiments on three real-world HSI datasets demonstrate that MPAS outperforms other state-of-the-art networks, demonstrating its superior feature learning capabilities.…”
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Research Application of Artificial Intelligence in Agricultural Risk Management: A Review by GUI Zechun, ZHAO Sijian

“…Finally, the applications of AI in agricultural risk management were prospected: In the future, AI algorithm could be considered in the construction of agricultural vulnerability curve; In view of the relationship between upstream and downstream of agricultural industry chain and agriculture-related industries, the graph neural network can be used more in the future to further study the agricultural price risk prediction; In the modeling process of future damage assessment, more professional knowledge related to the assessment target can be introduced to enhance the feature learning of the target, and expanding the small sample data is also the key subject of future research.…”
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Graph Sampling-Based Multi-Stream Enhancement Network for Visible-Infrared Person Re-Identification by Jinhua Jiang, Junjie Xiao, Renlin Wang, Tiansong Li, Wenfeng Zhang, Ruisheng Ran, Sen Xiang

“…Existing VI Re-ID methods focus on cross-modal feature learning and modal transformation to alleviate the discrepancy but overlook the impact of person contour information. …”
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