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1021
Siam Deep Feature KCF Method and Experimental Study for Pedestrian Tracking
Published 2023-01-01“…In addition, a lightweight Siamese CNN with Cross Stage Partial (CSP) provided the representations of features learned from massive face images, allowing the target similarity in data association to be guaranteed. …”
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1022
EvalAttAI: A Holistic Approach to Evaluating Attribution Maps in Robust and Non-Robust Models
Published 2023-01-01“…At the same time, numerous recent research papers in the fields of machine learning and explainable artificial intelligence have demonstrated the essential role of robustness to natural noise and adversarial attacks in determining the features learned by a model. This paper focuses on evaluating methods of attribution mapping to find whether robust neural networks are more explainable, particularly within the application of classification for medical imaging. …”
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1023
Local Feature Extraction Network for Point Cloud Analysis
Published 2021-02-01“…In this paper, we propose local feature extraction network (LFE-Net) which focus on extracting local feature for point clouds analysis. Such geometric features learning from a relation of local points can be used in a variety of shape analysis problems such as classification, part segmentation, and point matching. …”
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1024
Deep Learning for Sensing Matrix Prediction in Computational Microwave Imaging With Coded-Apertures
Published 2024-01-01“…To achieve this, a deep learning-based approach which is capable of generating the sensing matrix using features learned directly from the coded-aperture distribution is proposed. …”
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1025
A Systematic Approach for Explaining Time and Frequency Features Extracted by Convolutional Neural Networks From Raw Electroencephalography Data
Published 2022-05-01“…As such, a growing group of methods have been developed that provide insight into the spectral features learned by CNNs. However, spectral power is not the only important form of information within EEG, and the capacity to understand the roles of specific multispectral waveforms identified by CNNs could be very helpful. …”
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1026
Transformer-based autoencoder with ID constraint for unsupervised anomalous sound detection
Published 2023-10-01“…However, the AE-based methods could be limited as the feature learned from normal sounds can also fit with anomalous sounds, reducing the ability of the model in detecting anomalies from sound. …”
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1027
An Environmental Pattern Recognition Method for Traditional Chinese Settlements Using Deep Learning
Published 2023-04-01“…In this paper, we study the use of deep learning techniques to achieve automatic recognition of environmental patterns in TCSs based on environmental features learned from remote sensing images and digital elevation models. …”
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1028
Eye Tracking for Everyone
Published 2017“…Further, we demonstrate that the features learned by iTracker generalize well to other datasets, achieving state-of-the-art results.…”
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1029
Deep clustering of protein folding simulations
Published 2018-12-01“…In these systems, we show that the CVAE latent features learned correspond to distinct conformational substates along the protein folding pathways. …”
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1030
Automatic Feature Selection for Improved Interpretability on Whole Slide Imaging
Published 2021-02-01“…After training two WSI classification architectures on Camelyon-16 WSI dataset, highlighting discriminative features learned, and validating our approach with pathologists, we propose a novel manner of computing interpretability slide-level heat-maps, based on the extracted features, that improves tile-level classification performances. …”
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1031
A Two-Branch CNN Fusing Temporal and Frequency Features for Motor Imagery EEG Decoding
Published 2022-03-01“…TBTF-CNN fuses the features learned from the two branches and then inputs them into the classifier to decode the MI-EEG. …”
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1032
Aspect-level sentiment classification with fused local and global context
Published 2023-12-01“…To address these issues, we present the PConvBERT (Prompt-ConvBERT) and PConvRoBERTa (Prompt-ConvRoBERTa) models, in which local context features learned by a Local Semantic Feature Extractor (LSFE) are fused with the BERT/RoBERTa global features. …”
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1033
Network-based prediction approach for cancer-specific driver missense mutations using a graph neural network
Published 2023-10-01“…The prediction part learns whether variants are driver variants using features of individual variants combined with the graph features learned in the graph part. Results Net-DMPred, which considers molecular networks, performed better than conventional methods. …”
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1034
Knowledge Graph Double Interaction Graph Neural Network for Recommendation Algorithm
Published 2022-12-01“…Finally, the label propagation algorithm is used to train the edge weights to assist entity features learning. Experiments on two real datasets commonly used in recommended algorithms were conducted and showed that the model is better than the existing baseline models. …”
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1035
Hybrid Siamese Network for Unconstrained Face Verification and Clustering under Limited Resources
Published 2020-08-01“…It reaches 99.1% on the Arabian faces dataset. Moreover, features learned by the proposed architecture are used in building a face clustering system that is based on an updated version of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). …”
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1036
Concatenated Modified LeNet Approach for Classifying Pneumonia Images
Published 2024-03-01“…This enhancement aims to boost the discriminative capacity of the features learned by the model. Furthermore, we integrate batch normalization to stabilize the training process and enhance performance within smaller, less complex, CNN architectures like LeNet. …”
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1037
Floral Categorization by Bumblebees: The Perceptual and the Functional
Published 2022-08-01“…Bees grouped flowers based on the relevant features learned through experience rather than just relying on perceptual similarity, but also do not immediately discount similar flowers during foraging, giving a preliminary insight into the role of function in perceptual similarity judgments.…”
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1038
A Scale Sequence Object-based Convolutional Neural Network (SS-OCNN) for crop classification from fine spatial resolution remotely sensed imagery
Published 2021-11-01“…This scale sequence can fuse effectively the features learned at different scales by transforming progressively the information extracted at small scales to larger scales. …”
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1039
Real-Time Fault Diagnosis for Hydraulic System Based on Multi-Sensor Convolutional Neural Network
Published 2024-01-01“…Both the sensor selection process and fault diagnosis process are based on abstract fault-related features learned by a CNN deep learning model. Therefore, compared with the traditional sensor-and-feature selection method, the proposed MS-CNN can find the sensor channels containing higher-level fault-related features, which provides two advantages for diagnosis. …”
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1040
Feature fusion-based collaborative learning for knowledge distillation
Published 2021-11-01“…We concatenate the features learned by the teacher and the student networks to obtain a more representative feature map for knowledge transfer. …”
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