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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自建燃气电站、小型核电、风光储微电网，初始投入动辄数十亿美元。电力从“按月缴费的可变成本”，变成“先砸钱再谈算力”的刚性前置投入。中小AI公司直接失去入场资格，行业将进一步向拥有能源资本的巨头集中。
One of the most useful contributions is feedback on what does or doesn’t work, so please: try out GtkSvg, and tell us if you find SVGs that are rendered badly or with poor performance!
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