A mixture-of-experts transformer foundation model pretrained on diverse SEM images enables generalization across materials and outperforms SOTA on unsupervised defocus-to-focus restoration.
Journal of Machine Learning Research23(120), 1–39 (2022)
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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HMR-Net introduces hierarchical routing with global dataset-level and local scene-level modularity plus conditional experts to improve cross-domain aerial object detection and enable novel category recognition without retraining.
citing papers explorer
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A Mixture of Experts Foundation Model for Scanning Electron Microscopy Image Analysis
A mixture-of-experts transformer foundation model pretrained on diverse SEM images enables generalization across materials and outperforms SOTA on unsupervised defocus-to-focus restoration.
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HMR-Net: Hierarchical Modular Routing for Cross-Domain Object Detection in Aerial Images
HMR-Net introduces hierarchical routing with global dataset-level and local scene-level modularity plus conditional experts to improve cross-domain aerial object detection and enable novel category recognition without retraining.