GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification | Paper Citation Record

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This paper cites New satellite market forecast anticipates 1,700 satellites to be launched on average per year by 2030 as new entrants and incumbents increase their investment in space,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification New satellite market forecast anticipates 1,700 satellites to be launched on average per year by 2030 as new entrants and incumbents increase their investment in space,

Reference 1

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This paper cites A survey on nongeostationary satellite systems: The communication perspective,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification A survey on nongeostationary satellite systems: The communication perspective,

Reference 2

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This paper cites Connecting space missions through ngso con- stellations: feasibility study,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Connecting space missions through ngso con- stellations: feasibility study,

Reference 3

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This paper cites Artificial Intelligence for Satellite Communication and Non-Terrestrial Networks: A Survey.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Artificial Intelligence for Satellite Communication and Non-Terrestrial Networks: A Survey

Reference 4

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This paper cites The ϕ-sat-1 mission: The first on-board deep neural network demonstrator for satellite earth observation,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification The ϕ-sat-1 mission: The first on-board deep neural network demonstrator for satellite earth observation,

Reference 5

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This paper cites Artificial intelligence based on-board image compression for the ϕ-sat-2 mission,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Artificial intelligence based on-board image compression for the ϕ-sat-2 mission,

Reference 6

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This paper cites On-Air Deep Learning Integrated Semantic Inference Models for Enhanced Earth Observation Satellite Networks.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification On-Air Deep Learning Integrated Semantic Inference Models for Enhanced Earth Observation Satellite Networks

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This paper cites Onboard Satellite Image Classification for Earth Observation: A Comparative Study of ViT Models.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Onboard Satellite Image Classification for Earth Observation: A Comparative Study of ViT Models

Reference 8

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This paper cites Balancing practical uses and ethical concerns: The role of large language models in scientific research,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Balancing practical uses and ethical concerns: The role of large language models in scientific research,

Reference 9

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This paper cites A comparison between vgg16, vgg19 and resnet50 architecture frameworks for image classification,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification A comparison between vgg16, vgg19 and resnet50 architecture frameworks for image classification,

Reference 10

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Observation 63368f1e-7dab-4aad-ae0b-33e57804fd14 · outbound

This paper cites Battle of the backbones: A large-scale comparison of pretrained models across computer vision tasks,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Battle of the backbones: A large-scale comparison of pretrained models across computer vision tasks,

Reference 11

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This paper cites Exploring the synergies of hybrid convolutional neural network and vision transformer architectures for computer vision: A survey,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Exploring the synergies of hybrid convolutional neural network and vision transformer architectures for computer vision: A survey,

Reference 12

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This paper cites Distilling the Knowledge in a Neural Network.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Distilling the Knowledge in a Neural Network

Reference 13

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This paper cites Semantic knowledge distillation for onboard satel- lite earth observation image classification,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Semantic knowledge distillation for onboard satel- lite earth observation image classification,

Reference 14

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Observation e1a79dd7-8151-4ba4-aae9-6cbf37560fe0 · outbound

This paper cites GLU Variants Improve Transformer.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification GLU Variants Improve Transformer

Reference 15

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This paper cites Squeeze-and-excitation networks,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Squeeze-and-excitation networks,

Reference 16

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This paper cites Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification,

Reference 17

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This paper cites Patternnet: A benchmark dataset for performance evaluation of remote sensing image retrieval,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Patternnet: A benchmark dataset for performance evaluation of remote sensing image retrieval,

Reference 18

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Observation 56f9fb4e-f3e0-450d-a536-2d1ffa49b9f4 · outbound

This paper cites Adaptive feature recombination and recalibration for semantic segmentation with fully convolutional networks,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Adaptive feature recombination and recalibration for semantic segmentation with fully convolutional networks,

Reference 19

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This paper cites Aggregated dense layer in squeeze and excitation networks,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Aggregated dense layer in squeeze and excitation networks,

Reference 20

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This paper cites Gcnet: Non-local networks meet squeeze-excitation networks and beyond,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Gcnet: Non-local networks meet squeeze-excitation networks and beyond,

Reference 21

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This paper cites Training spiking neural networks using lessons from deep learning,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Training spiking neural networks using lessons from deep learning,

Reference 22

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GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification The neurobench framework for benchmarking neuro- morphic computing algorithms and systems,

Reference 23

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This paper cites Energy-efficient on-board radio resource manage- ment for satellite communications via neuromorphic computing,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Energy-efficient on-board radio resource manage- ment for satellite communications via neuromorphic computing,

Reference 24

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Observation 77b7b973-8955-4dea-a0d6-96a1a811d058 · outbound

This paper cites Language modeling with gated convolutional networks,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Language modeling with gated convolutional networks,

Reference 25

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Observation d1f6c304-9e7e-4402-ac82-0d3780359793 · outbound

This paper cites Gated Transformer Networks for Multivariate Time Series Classification.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Gated Transformer Networks for Multivariate Time Series Classification

Reference 26

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This paper cites Temporal fusion transform- ers for interpretable multi-horizon time series forecasting,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Temporal fusion transform- ers for interpretable multi-horizon time series forecasting,

Reference 27

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This paper cites Transformer Meets Gated Residual Networks To Enhance Photoplethysmogram Artifact Detection Informed by Mutual Information Neural Estimation.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Transformer Meets Gated Residual Networks To Enhance Photoplethysmogram Artifact Detection Informed by Mutual Information Neural Estimation

Reference 28

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This paper cites Empirical analysis of squeeze and excitation-based densely connected cnn for chili leaf disease identification,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Empirical analysis of squeeze and excitation-based densely connected cnn for chili leaf disease identification,

Reference 29

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This paper cites Lightweight spectral–spatial squeeze-and- excitation residual bag-of-features learning for hyperspectral classification,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Lightweight spectral–spatial squeeze-and- excitation residual bag-of-features learning for hyperspectral classification,

Reference 30

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This paper cites Eca-net: Efficient channel attention for deep convolu- tional neural networks,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Eca-net: Efficient channel attention for deep convolu- tional neural networks,

Reference 31

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This paper cites A survey on efficient vision transformers: algorithms, techniques, and performance benchmarking,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification A survey on efficient vision transformers: algorithms, techniques, and performance benchmarking,

Reference 32

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This paper cites Does knowledge distillation really work?.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Does knowledge distillation really work?

Reference 33

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This paper cites R ´enyi divergence and kullback-leibler divergence,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification R ´enyi divergence and kullback-leibler divergence,

Reference 34

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This paper cites Knowledge distillation IEEE, VOL., NO., 2025. 12 Fig. 8: Confusion matrix from ResNet-GLUSE with KD on the PatternNet dataset. from a stronger teacher,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Knowledge distillation IEEE, VOL., NO., 2025. 12 Fig. 8: Confusion matrix from ResNet-GLUSE with KD on the PatternNet dataset. from a stronger teacher,

Reference 35

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This paper cites A Semantic-Loss Function Modeling Framework With Task-Oriented Machine Learning Perspectives.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification A Semantic-Loss Function Modeling Framework With Task-Oriented Machine Learning Perspectives

Reference 36

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This paper cites Efficientvit: Memory efficient vision transformer with cascaded group attention,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Efficientvit: Memory efficient vision transformer with cascaded group attention,

Reference 37

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This paper cites Separable self-attention for mobile vision transformers,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Separable self-attention for mobile vision transformers,

Reference 38

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This paper cites Scikit-learn: Machine learning in Python,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Scikit-learn: Machine learning in Python,

Reference 39

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This paper cites Autoaug- ment: Learning augmentation strategies from data,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Autoaug- ment: Learning augmentation strategies from data,

Reference 40

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This paper cites A probabilistic interpretation of precision, recall and f-score, with implication for evaluation,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification A probabilistic interpretation of precision, recall and f-score, with implication for evaluation,

Reference 41

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This paper cites Accessed: March 13, 2025.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Accessed: March 13, 2025

Reference 42

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This paper cites Grad-CAM: Visual explanations from deep networks via gradient-based localization,.

GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification Grad-CAM: Visual explanations from deep networks via gradient-based localization,

Reference 43

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