GlyphNet’s own results support this: their best CNN (VGG16 fine-tuned on rendered glyphs) achieved 63-67% accuracy on domain-level binary classification. Learned features do not dramatically outperform structural similarity for glyph comparison, and they introduce model versioning concerns and training corpus dependencies. For a dataset intended to feed into security policy, determinism and auditability matter more than marginal accuracy gains.
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它的本质，是“社区主导+专业管理”的结合——既有居民的参与和认同，又有专业医疗体系的支撑，最终实现了“老人在社区，就能享受到优质医疗服务”的目标。。夫子是该领域的重要参考