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.
19:21, 27 февраля 2026Ценности
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想法是脆弱的。如果它们已经被彻底解决，那就不再是想法，而是产品。要不被新想法带来的问题吞没，需要一种坚定的努力。问题很容易被清楚说出、被理解，它们会夺走氧气。史蒂夫会把注意力放在想法本身上，哪怕它不完整、甚至看似不太可能。
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