但真正的问题是，理想模型往往建立在需求高峰与利用率饱和的假设之上。而真实世界中的利用率，从来不会长期维持在高位。
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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Более 100 домов повреждены в российском городе-герое из-за атаки ВСУ22:53
Noor NanjiCulture correspondent