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881
GMFAD: Towards generalized visual recognition via multilayer feature alignment and disentanglement
Published 2022“…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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Journal Article -
882
Learning the structure of object categories from incomplete supervision
Published 2018“…</p> <p>A similar feature learning machine leveraging the equivariance constraint is later introduced. …”
Thesis -
883
Improved deep learning techniques for recognition and labeling
Published 2017“…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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Thesis -
884
Noise eliminated ensemble empirical mode decomposition scalogram analysis for rotating machinery fault diagnosis
Published 2022“…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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Thesis -
885
Phenotyping agronomic and physiological traits in peanut under mid‐season drought stress using UAV‐based hyperspectral imaging and machine learning
Published 2023-12-01“…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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Article -
886
Spatio-Temporal Features Representation Using Recurrent Capsules for Monaural Speech Enhancement
Published 2024-01-01“…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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Article -
887
A twin convolutional neural network with hybrid binary optimizer for multimodal breast cancer digital image classification
Published 2024-01-01“…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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Article -
888
TCFLTformer: TextCNN-Flat-Lattice Transformer for Entity Recognition of Air Traffic Management Cyber Threat Knowledge Graphs
Published 2023-08-01“…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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Article -
889
TasselLFANet: a novel lightweight multi-branch feature aggregation neural network for high-throughput image-based maize tassels detection and counting
Published 2023-04-01“…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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Article -
890
Early Detection of Wheat Yellow Rust Disease and Its Impact on Terminal Yield with Multi-Spectral UAV-Imagery
Published 2023-06-01“…A custom 3-dimensional convolutional neural network (3D-CNN) relying on the feature learning mechanism was an alternative prediction method. …”
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Article -
891
A coarse-to-fine point completion network with details compensation and structure enhancement
Published 2024-01-01“…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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Article -
892
Learning Multi-Types of Neighbor Node Attributes and Semantics by Heterogeneous Graph Transformer and Multi-View Attention for Drug-Related Side-Effect Prediction
Published 2023-09-01“…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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Article -
893
Recognizing Semi-Natural and Spontaneous Speech Emotions Using Deep Neural Networks
Published 2022-01-01“…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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Article -
894
Explainable Artificial Intelligence (XAI) in Pain Research: Understanding the Role of Electrodermal Activity for Automated Pain Recognition
Published 2023-02-01“…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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Article -
895
An improved fused feature residual network for 3D point cloud data
Published 2023-08-01“…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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Article -
896
A New Dataset and Deep Residual Spectral Spatial Network for Hyperspectral Image Classification
Published 2020-04-01“…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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Article -
897
Self-Supervised Feature Representation for SAR Image Target Classification Using Contrastive Learning
Published 2023-01-01“…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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Article -
898
Multiscale Pixel-Level and Superpixel-Level Method for Hyperspectral Image Classification: Adaptive Attention and Parallel Multi-Hop Graph Convolution
Published 2023-08-01“…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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Article -
899
Research Application of Artificial Intelligence in Agricultural Risk Management: A Review
Published 2023-03-01“…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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Article -
900
Graph Sampling-Based Multi-Stream Enhancement Network for Visible-Infrared Person Re-Identification
Published 2023-09-01“…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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Article