Cross-modal distillation transfers spatial transcriptomics-derived tissue niche structures to histology-only models, yielding higher agreement with molecular niches than unsupervised image-feature baselines across tissues and diseases.
Fit- nets: Hints for thin deep nets
2 Pith papers cite this work. Polarity classification is still indexing.
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AsymLoc uses teacher-student distillation with geometry-driven matching to enable efficient nearest-neighbor feature matching, achieving 95% of teacher accuracy with 10x smaller models on localization benchmarks.
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Cross-Modal Knowledge Distillation from Spatial Transcriptomics to Histology
Cross-modal distillation transfers spatial transcriptomics-derived tissue niche structures to histology-only models, yielding higher agreement with molecular niches than unsupervised image-feature baselines across tissues and diseases.
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AsymLoc: Towards Asymmetric Feature Matching for Efficient Visual Localization
AsymLoc uses teacher-student distillation with geometry-driven matching to enable efficient nearest-neighbor feature matching, achieving 95% of teacher accuracy with 10x smaller models on localization benchmarks.