ARF-SFR-Net adaptively determines receptive field sizes to reconstruct spatial-frequency features and improve few-shot fine-grained image classification over prior methods.
arXiv preprint arXiv:2106.06988 (2021)
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GloResNet, a ResNet-10-based lightweight 3D CNN pretrained on MedicalNet with global manifold mapping for topology preservation, achieves 75.18% average accuracy (peak 81.82%) in 5-fold cross-validation for preterm brain injury prediction.
citing papers explorer
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Adaptive receptive field-based spatial-frequency feature reconstruction network for few-shot fine-grained image classification
ARF-SFR-Net adaptively determines receptive field sizes to reconstruct spatial-frequency features and improve few-shot fine-grained image classification over prior methods.
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GloResNet: A lightweight 3D CNN with global topological features for preterm brain injury prediction
GloResNet, a ResNet-10-based lightweight 3D CNN pretrained on MedicalNet with global manifold mapping for topology preservation, achieves 75.18% average accuracy (peak 81.82%) in 5-fold cross-validation for preterm brain injury prediction.